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nf7UWF60U2/E5jp7+VKEv/mFzM/uXufFdVGWkCOcBIxM1IyU3Cy3VOygWGw+T1wTouOIlj4zvD8bq7Tn74MrvkJSeSNI7uzncNQiVFIAcYj6uCG4oP070tZXdAmsnrg5TdHb+sOPgzu6hz1GaXvClVL5gwf59rKCkCOcAIzOaL1wV947fObgVaajt53W5ix8RCyX7yUdWsdDgrmGHQ5trW9nq4H2EeHiWQAwxcTJd9VfBTHuobB9q4N+QtW0b81YXJ652T3R2fxYC1KuI1l51HcNpimOAyr0uDC3mSQA4wYHHjUTI46U3NGyug/yIgHbaYvH5l8no3xEJutqqqC3MAsv3hOxuuAHMMGk8SyAGGrWQQzSx6QAGWEgMhS4PPfkyypr7steA6BqtVFf3rH5KPYZNHeXo98ySBHGCgZvPFaZyLmqFvHFip6d5WU53zsvBrPjN5va4YfJYdYtalwWXZx7JfOEhwmVBda9AdPSKQAwxQ9GK+LfjJjitOyAUGKk5raDN5fZO7mPxVdnf8tiPl6l9Eu1V2oGDJ66vtzzjbPrlxXeVJAjnAwEQPZskNxLvYyQJYuene1nGLyetvTV7fvLh+HCW/8UUHB4Bmd8mfRZ/8kyJUZ8vh79MCRopADjA8ZwXDeD65aQA2wOT1fijZOe7cOdvRz559nZX8rKWLSm+2T270j5MikAMMSOwoZXc3Gp8dcQasm8nr3RfPczakXna4zSlbIXYQ/fJP2j65+Xqc3yzx4S+3T24sdJMmkAMMRNxYlPa6HRQOtwFoxeT1zjssOJWjy4P3zgoWftLBuQnl2yc3TSj/KUrY73+PZlf+3WQy+ef2yU3nKgfotn/4/QAMxlXBzdR972fzxWfnjwPr1syrmM0XLwuHTn6ZvB696KxPNpzOuny9aKoxZvPFVbJa7MsAwZJKsShFd71kZeyQAwzAbL54XdA3/j1XejWBTTB5vXuiUmo/+cD6cCxdScm41xVVCeQAPReTYl8t+VPo1QQ2ZonJ63bJ1yP7vF7H4LROi/727GT0U9c+ahLIAXosbiJWdWSZXk1gk9pMXj9TzbNasag7TX7RPvVHZx/rTvTPQxUCOUC/XbTsG3/Il15Nrwlg3ZaYvP7RjuZKZXfHb3tSrv5F9LlnqzBMRacagRygp6Kf8mANj16vJrARLSevC+UrEqdzZI/KvOjhMZnZXfL9ODYUNk4gB+ihKNksmVJ8WVgaqlcT2Ihm8npzdnPh93rWs/LprirZGe7d8x397tkKDAvRVCGQA/RM7AqVlA1+igFKpaWhejWBjVhi8rpS45biWpLdFb6MQWl9lF1ImEY/PWyUQA7QP2cFR5zdfi0HjdLQkpI8ZaHAxrScvP5KNU9rpwUzSPo8W6RkZ99riY0TyAF6JHrcsv1+jdMI4l/EkJufCz5fKAc2yeT1zcmWaM/i2tFL0feerb44ir562BiBHKAn4iahZJfiw/fOi43/VlIaqlcT2AiT1zcjqgrGsDv+VUlrgzYINkogB+iPq4IbqOvHSu+iNHRW8JM3uwZCObB2Jq9vRDZ0Xn9vYbdvov89e8079DpikwRygB6IwUXZvvHGYeJ4msPCfs1f9GoCm2Dy+vpE69N+8hsM6fnMLkLsmLjOJgnkAB0XU19fFTzKl/f7xh8Sgf24sDT0vV5NBqCv06JHxeT1tcmGzduBlKt/EX3w2fkEAjkbI5ADdFiUzV0VPMJZ3MSmRHAvPeblo6E39FmPj28aHZPXVysWVKfJL3qRqLTqm/QuudcQmyKQA3TbRUHf+G3hsWZfRCgvnbx+pceuFUFwjbwmB8vk9dUp2fkdXPl/9MNnq8JUWrARAjlAR83mi+bG6aDg0WX6xr8rblLeFXzKs8Kde/4lE8hVH7QngA3QkpPX/T2FeC6yx2ZeDriSJLvQsB8tY7BWAjlAB8XOztuCR/Zu2XNip3tbzQLAh5JPmc0Xg+kv7BABYr1KQh0dscTkddU8/1ayOz7k9/aSnX+75KydQA7QMXHzWHIz9CnC9CqU9mse6bMrYod8vTK7WU8OPKSbYvJ6SXvNJKp5Rr9wGNeV7Hv1p2UXeLssKi6ywwKnWh9YN4EcoHvOCo44a9U3/pC4UXnRYvK6sr6cTCDPHkfE32UWM4Y2pGpUor2mdPL6wWy+GPtxaKcF80jG8FyV7HybuM5aCeQAHRLnw2Z7/Bqnq+7zaxnKr+wipKR2Zy1wtJZ5Ddoh77mYvD4r/Cl+GXk1T/Znv45Fj0GL62b2NXRkFgHrJJADdERc8EtuhC7XdeMU/ZoluwJ6NRNisSOz0GFxo51MZYlAPgyHLY5Dez/GhcNYiMhW3oypksAuOZ0gkAN0x1VBSeH1um8QIuy/KfiU5oZvsH2HK5QJhHbICxVUFTh6bgBiceu4xZC+MU5ez14rbsfUbx998tld8mMLzqyLQA7QAbP54nVB3/hkmSPOSkz3tl4X9ms+M3n9SZlFC4G8XGaWwm1UfzAA8bssnaExqmqeWKjKXlvONnFd6Zjs9WqnxZR/SBHIASqLG6ZXBY/i5SZDRfRrlk5eV973sMzvbkcfeTET1kcodjlNXn9Ytiz7dmTl6l9EJdh18sNd11gLgRygotiluSp4BB+me1s1bppeFNy0NN7GgDr+LlvW7/lLihLkzC5gyd8aPWHy+vfF38U0+eFXI9wd/yq7aLHvmE/WQSAHqOuioG/8tlbJXNyoHRb2a16YvP538VxmKg7c+OVld67MOBgok9e/q2RoWcnHDopdcmoTyAEqibLug4LvvpG+8YdEmXzJzetODFAyCOfvMuWyO6oM0jLP07X+8cEzeT3E7nj2CM3LVR+f2UPZFoZn2olYNYEcoIK4AXxb8J3fRK9kVdO9rabk92XBYxDKvy9bOm035gmxaJE50km5+sCZvP4XJYuno90dv+es4HWz0ufr7nz3h7vz3eO7892Lu/Pdj/HvKv6ba+cICOQAGxbhtGSg0KeYdt4J0cNeNHl9jMOCHhO7UZmdvOkIj2gqlV20MP1/BExe//Mak/27mNkd/3MxJ3udWtn78t357mEcxfg+Khqm8e8g/tvnJpiv4nvRXQI5wOadFRxDc9vF4V4t+jWP4mg3/i0bED1vD4jS0czQKuXqI7LE5PWhVFEcF8wm8f7ybyULx0s/bxG0//uJ31Xz/3t/d77r9zRgAjnABkV5bbavr3Hc4d2L0n7NVybU/kU2kB/ZJX9Q9ibV7vjItJy83ux8DuG1UrI7btBhiF3y7Gtmqfflu/PdF7EDnvUqdtMZIIEcYEPi4l1ys3cZPdud1LJfc5ADlNoovPlT8v+Ngt3xiedvnFpOXj/q88JhPPbMTIWJv4vvKtmJXuZ10mbhx+9roARygM25Kigj/NSHgV4t+zWHOECprexN2YHJvn+Tfe4uR3y+Mu0nr/f17y0bEq+7vOBbS1SkZRdKT9vMHbg7331esGhy375d8mESyAE247Sgb3wSpeq9CBEt+jUHNUBpGfHcZXfwLjxn/xLzCLI3tHovR2yJyetXfavmKawa8XfxsOxO9E7LGS/LhGoVZgMkkANsRnZnvPGybwOool/zXcGnPNPX+6fsjfG+m+g/jwx8lfxw5yuzzOT15j2qT4tg2aqq63jP5jvi9ZJdKB39ezLLE8gBuuVDHCvWO9O9reZm8EPB4z4YyAClpRTukv8y5tL1FkcGulnmiyUmr/fiPSragA6SH64X+WnphVLDSlmWQA7QHddLDonpguPCfs1eD1BaoZLnYMzl/iVHBr6zO859LSevl1Q31ZQNkLeqk55WuFBaeg1b5n3JPIwBEsgBuqM3feMPicd/2GLy+qgHlkVwzJb878RgvFGF8li4yR4ZeGt3nO9pOXm90+K9IPu3cWbIYVp24WJaeA1b5qg5g/gGSCAH6IY3QzkPNsLli8JQ3rsBSmvwOqokMkbVgx9hvOTM3t4vbrFWbSavd1m2d/xWuXpeVFRk35PTp6Jsn9yUTHK/bxafy8AI5AD1zaZ7W4PazYuhOCXHtu2MfYr4vWnQWaPowY+FmpIw/sFxTjxmicnrnRPvmdn32isLVcWy1+aDwuM8Twtff7cDaGnjAQI5QF2DvcjG7sKbgk95tmQpX+9FlUTJtPqjIYfyKAMteU0MYQ4DG9By8noXHRb0uWvjKFS4S55+frdPbn6PSrLM127uE17YHR8ugRygruMhD56Knf+S0rxnY5+8HtPqSwfjDW7QW5Sp/1o4VOvQDiBZLSevd002BDoCsL3sNemo5H14++TmtzhX/N0Du+W3cf38MT6WgRLIAep5N5LS2jYBs6TcfYhKe/APhjTobTZfvC4sU2/83Lfz+6mv5eT1TpjNF83u+H7ysdgdb++s4P246NrV7JRvn9ycbp/cNO/dP8UCUfPvp+a/bZ/cHMduOgMmkAPU8WksN0ixY1kaMN/GzeYotXzOmpL/z32eWN8sKDS7/ZPJ5FXhp76LYAXFYvL6hx4+c9nwN7M73l68H2eH4bVeTN4+ufm4fXJzEf9G3b41NgI5wObdjm0KdMuAeTHmyeux21sarpvy7l9jh7lXYiHht9jtL3EZZf6wjOM+TV6Pv5dp8sPtji8vG8h3ot0G0gRygM17PcbS2viZS25URnne9n3xnLXpcX01my9+68OCRuyKn0W/eLb89qvL2N2EpcSi4WGPJq9nX/ezoRypWVO8PrKtDRZAKCKQA2xWcyTTaM+BjZ75lwWfIpT/qxS7TShvStj/pxmS19XnL3aSmkWHX1p8ujDOSkVZd+dbPuJ4raPkhzt3fHWyQXt/zC1XlBPIATbHkUz/uuk9K528PvabynuhvM3u3VH0lr/uSjBvym2bHfwY3Fa6Kz4RxlmXJapSNikbDK+dyb86sWCTvXZpoyFNIAfYHEcyhQhTpZPXR10GGKG8tA//q50YlPY5dsx/XM+jfFiUph/P5ovPUZ7+rOWXEsZZq/hbe9fFZzkW1bK7r0qnVy+7ODzt84BNNksgB9iMN4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)}.socialMediaLinks{color:#fff;flex-wrap:wrap;justify-content:space-around}.DocSearch-Button,.DocSearch-Button-Container{align-items:center;display:flex}.DocSearch-Button{background:var(--docsearch-searchbox-background);border:0;border-radius:40px;color:var(--docsearch-muted-color);cursor:pointer;font-weight:500;height:36px;justify-content:space-between;padding:0 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var(--ifm-transition-timing-default)}.pagination-nav__link:hover{border-color:var(--ifm-pagination-nav-color-hover);text-decoration:none}.pagination-nav__link--next{grid-column:2/3;text-align:right}.pagination-nav__label{font-size:var(--ifm-h4-font-size);font-weight:var(--ifm-heading-font-weight);word-break:break-word}.pagination-nav__link--prev .pagination-nav__label:before{content:"« "}.pagination-nav__link--next .pagination-nav__label:after{content:" »"}.pagination-nav__sublabel{color:var(--ifm-color-content-secondary);font-size:var(--ifm-h5-font-size);font-weight:var(--ifm-font-weight-semibold);margin-bottom:.25rem}.pills__item,.tabs{font-weight:var(--ifm-font-weight-bold)}.pills{display:flex;gap:var(--ifm-pills-spacing);padding-left:0}.pills__item{border-radius:.5rem;cursor:pointer;display:inline-block;padding:.25rem 1rem;transition:background var(--ifm-transition-fast) 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)}.socialMediaLinks{color:#fff;flex-wrap:wrap;justify-content:space-around}.skipToContent_fXgn{background-color:var(--ifm-background-surface-color);color:var(--ifm-color-emphasis-900);left:100%;padding:calc(var(--ifm-global-spacing)/2) 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It is probably an error, if this is really what you want, you have to first clear the numeric filters."):null},m.prototype={constructor:m,clearRefinements:function(e){var t={numericRefinements:this._clearNumericRefinements(e),facetsRefinements:f.clearRefinement(this.facetsRefinements,e,"conjunctiveFacet"),facetsExcludes:f.clearRefinement(this.facetsExcludes,e,"exclude"),disjunctiveFacetsRefinements:f.clearRefinement(this.disjunctiveFacetsRefinements,e,"disjunctiveFacet"),hierarchicalFacetsRefinements:f.clearRefinement(this.hierarchicalFacetsRefinements,e,"hierarchicalFacet")};return t.numericRefinements===this.numericRefinements&&t.facetsRefinements===this.facetsRefinements&&t.facetsExcludes===this.facetsExcludes&&t.disjunctiveFacetsRefinements===this.disjunctiveFacetsRefinements&&t.hierarchicalFacetsRefinements===this.hierarchicalFacetsRefinements?this:this.setQueryParameters(t)},clearTags:function(){return void 0===this.tagFilters&&0===this.tagRefinements.length?this:this.setQueryParameters({tagFilters:void 0,tagRefinements:[]})},setIndex:function(e){return e===this.index?this:this.setQueryParameters({index:e})},setQuery:function(e){return e===this.query?this:this.setQueryParameters({query:e})},setPage:function(e){return e===this.page?this:this.setQueryParameters({page:e})},setFacets:function(e){return this.setQueryParameters({facets:e})},setDisjunctiveFacets:function(e){return this.setQueryParameters({disjunctiveFacets:e})},setHitsPerPage:function(e){return this.hitsPerPage===e?this:this.setQueryParameters({hitsPerPage:e})},setTypoTolerance:function(e){return this.typoTolerance===e?this:this.setQueryParameters({typoTolerance:e})},addNumericRefinement:function(e,t,r){var n=o(r);if(this.isNumericRefined(e,t,n))return this;var i=s({},this.numericRefinements);return i[e]=s({},i[e]),i[e][t]?(i[e][t]=i[e][t].slice(),i[e][t].push(n)):i[e][t]=[n],this.setQueryParameters({numericRefinements:i})},getConjunctiveRefinements:function(e){return this.isConjunctiveFacet(e)&&this.facetsRefinements[e]||[]},getDisjunctiveRefinements:function(e){return this.isDisjunctiveFacet(e)&&this.disjunctiveFacetsRefinements[e]||[]},getHierarchicalRefinement:function(e){return this.hierarchicalFacetsRefinements[e]||[]},getExcludeRefinements:function(e){return this.isConjunctiveFacet(e)&&this.facetsExcludes[e]||[]},removeNumericRefinement:function(e,t,r){var n=r;return void 0!==n?this.isNumericRefined(e,t,n)?this.setQueryParameters({numericRefinements:this._clearNumericRefinements((function(r,i){return i===e&&r.op===t&&l(r.val,o(n))}))}):this:void 0!==t?this.isNumericRefined(e,t)?this.setQueryParameters({numericRefinements:this._clearNumericRefinements((function(r,n){return n===e&&r.op===t}))}):this:this.isNumericRefined(e)?this.setQueryParameters({numericRefinements:this._clearNumericRefinements((function(t,r){return r===e}))}):this},getNumericRefinements:function(e){return this.numericRefinements[e]||{}},getNumericRefinement:function(e,t){return this.numericRefinements[e]&&this.numericRefinements[e][t]},_clearNumericRefinements:function(e){if(void 0===e)return c(this.numericRefinements)?{}:this.numericRefinements;if("string"==typeof e)return u(this.numericRefinements,[e]);if("function"==typeof e){var t=!1,r=this.numericRefinements,n=Object.keys(r).reduce((function(n,i){var a=r[i],s={};return a=a||{},Object.keys(a).forEach((function(r){var n=a[r]||[],c=[];n.forEach((function(t){e({val:t,op:r},i,"numeric")||c.push(t)})),c.length!==n.length&&(t=!0),s[r]=c})),n[i]=s,n}),{});return t?n:this.numericRefinements}},addFacet:function(e){return this.isConjunctiveFacet(e)?this:this.setQueryParameters({facets:this.facets.concat([e])})},addDisjunctiveFacet:function(e){return this.isDisjunctiveFacet(e)?this:this.setQueryParameters({disjunctiveFacets:this.disjunctiveFacets.concat([e])})},addHierarchicalFacet:function(e){if(this.isHierarchicalFacet(e.name))throw new Error("Cannot declare two hierarchical facets with the same name: `"+e.name+"`");return this.setQueryParameters({hierarchicalFacets:this.hierarchicalFacets.concat([e])})},addFacetRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return f.isRefined(this.facetsRefinements,e,t)?this:this.setQueryParameters({facetsRefinements:f.addRefinement(this.facetsRefinements,e,t)})},addExcludeRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return f.isRefined(this.facetsExcludes,e,t)?this:this.setQueryParameters({facetsExcludes:f.addRefinement(this.facetsExcludes,e,t)})},addDisjunctiveFacetRefinement:function(e,t){if(!this.isDisjunctiveFacet(e))throw new Error(e+" is not defined in the disjunctiveFacets attribute of the helper configuration");return f.isRefined(this.disjunctiveFacetsRefinements,e,t)?this:this.setQueryParameters({disjunctiveFacetsRefinements:f.addRefinement(this.disjunctiveFacetsRefinements,e,t)})},addTagRefinement:function(e){if(this.isTagRefined(e))return this;var t={tagRefinements:this.tagRefinements.concat(e)};return this.setQueryParameters(t)},removeFacet:function(e){return this.isConjunctiveFacet(e)?this.clearRefinements(e).setQueryParameters({facets:this.facets.filter((function(t){return t!==e}))}):this},removeDisjunctiveFacet:function(e){return this.isDisjunctiveFacet(e)?this.clearRefinements(e).setQueryParameters({disjunctiveFacets:this.disjunctiveFacets.filter((function(t){return t!==e}))}):this},removeHierarchicalFacet:function(e){return this.isHierarchicalFacet(e)?this.clearRefinements(e).setQueryParameters({hierarchicalFacets:this.hierarchicalFacets.filter((function(t){return t.name!==e}))}):this},removeFacetRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return f.isRefined(this.facetsRefinements,e,t)?this.setQueryParameters({facetsRefinements:f.removeRefinement(this.facetsRefinements,e,t)}):this},removeExcludeRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return f.isRefined(this.facetsExcludes,e,t)?this.setQueryParameters({facetsExcludes:f.removeRefinement(this.facetsExcludes,e,t)}):this},removeDisjunctiveFacetRefinement:function(e,t){if(!this.isDisjunctiveFacet(e))throw new Error(e+" is not defined in the disjunctiveFacets attribute of the helper configuration");return f.isRefined(this.disjunctiveFacetsRefinements,e,t)?this.setQueryParameters({disjunctiveFacetsRefinements:f.removeRefinement(this.disjunctiveFacetsRefinements,e,t)}):this},removeTagRefinement:function(e){if(!this.isTagRefined(e))return this;var t={tagRefinements:this.tagRefinements.filter((function(t){return t!==e}))};return this.setQueryParameters(t)},toggleRefinement:function(e,t){return this.toggleFacetRefinement(e,t)},toggleFacetRefinement:function(e,t){if(this.isHierarchicalFacet(e))return this.toggleHierarchicalFacetRefinement(e,t);if(this.isConjunctiveFacet(e))return this.toggleConjunctiveFacetRefinement(e,t);if(this.isDisjunctiveFacet(e))return this.toggleDisjunctiveFacetRefinement(e,t);throw new Error("Cannot refine the undeclared facet "+e+"; it should be added to the helper options facets, disjunctiveFacets or hierarchicalFacets")},toggleConjunctiveFacetRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return this.setQueryParameters({facetsRefinements:f.toggleRefinement(this.facetsRefinements,e,t)})},toggleExcludeFacetRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return this.setQueryParameters({facetsExcludes:f.toggleRefinement(this.facetsExcludes,e,t)})},toggleDisjunctiveFacetRefinement:function(e,t){if(!this.isDisjunctiveFacet(e))throw new Error(e+" is not defined in the disjunctiveFacets attribute of the helper configuration");return this.setQueryParameters({disjunctiveFacetsRefinements:f.toggleRefinement(this.disjunctiveFacetsRefinements,e,t)})},toggleHierarchicalFacetRefinement:function(e,t){if(!this.isHierarchicalFacet(e))throw new Error(e+" is not defined in the hierarchicalFacets attribute of the helper configuration");var r=this._getHierarchicalFacetSeparator(this.getHierarchicalFacetByName(e)),i={};return void 0!==this.hierarchicalFacetsRefinements[e]&&this.hierarchicalFacetsRefinements[e].length>0&&(this.hierarchicalFacetsRefinements[e][0]===t||0===this.hierarchicalFacetsRefinements[e][0].indexOf(t+r))?-1===t.indexOf(r)?i[e]=[]:i[e]=[t.slice(0,t.lastIndexOf(r))]:i[e]=[t],this.setQueryParameters({hierarchicalFacetsRefinements:n({},i,this.hierarchicalFacetsRefinements)})},addHierarchicalFacetRefinement:function(e,t){if(this.isHierarchicalFacetRefined(e))throw new Error(e+" is already refined.");if(!this.isHierarchicalFacet(e))throw new Error(e+" is not defined in the hierarchicalFacets attribute of the helper configuration.");var r={};return r[e]=[t],this.setQueryParameters({hierarchicalFacetsRefinements:n({},r,this.hierarchicalFacetsRefinements)})},removeHierarchicalFacetRefinement:function(e){if(!this.isHierarchicalFacetRefined(e))return this;var t={};return t[e]=[],this.setQueryParameters({hierarchicalFacetsRefinements:n({},t,this.hierarchicalFacetsRefinements)})},toggleTagRefinement:function(e){return this.isTagRefined(e)?this.removeTagRefinement(e):this.addTagRefinement(e)},isDisjunctiveFacet:function(e){return this.disjunctiveFacets.indexOf(e)>-1},isHierarchicalFacet:function(e){return void 0!==this.getHierarchicalFacetByName(e)},isConjunctiveFacet:function(e){return this.facets.indexOf(e)>-1},isFacetRefined:function(e,t){return!!this.isConjunctiveFacet(e)&&f.isRefined(this.facetsRefinements,e,t)},isExcludeRefined:function(e,t){return!!this.isConjunctiveFacet(e)&&f.isRefined(this.facetsExcludes,e,t)},isDisjunctiveFacetRefined:function(e,t){return!!this.isDisjunctiveFacet(e)&&f.isRefined(this.disjunctiveFacetsRefinements,e,t)},isHierarchicalFacetRefined:function(e,t){if(!this.isHierarchicalFacet(e))return!1;var r=this.getHierarchicalRefinement(e);return t?-1!==r.indexOf(t):r.length>0},isNumericRefined:function(e,t,r){if(void 0===r&&void 0===t)return Boolean(this.numericRefinements[e]);var n=this.numericRefinements[e]&&void 0!==this.numericRefinements[e][t];if(void 0===r||!n)return n;var a,s,c=o(r),u=void 0!==(a=this.numericRefinements[e][t],s=c,i(a,(function(e){return l(e,s)})));return n&&u},isTagRefined:function(e){return-1!==this.tagRefinements.indexOf(e)},getRefinedDisjunctiveFacets:function(){var e=this,t=a(Object.keys(this.numericRefinements).filter((function(t){return Object.keys(e.numericRefinements[t]).length>0})),this.disjunctiveFacets);return Object.keys(this.disjunctiveFacetsRefinements).filter((function(t){return e.disjunctiveFacetsRefinements[t].length>0})).concat(t).concat(this.getRefinedHierarchicalFacets()).sort()},getRefinedHierarchicalFacets:function(){var e=this;return a(this.hierarchicalFacets.map((function(e){return e.name})),Object.keys(this.hierarchicalFacetsRefinements).filter((function(t){return e.hierarchicalFacetsRefinements[t].length>0}))).sort()},getUnrefinedDisjunctiveFacets:function(){var e=this.getRefinedDisjunctiveFacets();return this.disjunctiveFacets.filter((function(t){return-1===e.indexOf(t)}))},managedParameters:["index","facets","disjunctiveFacets","facetsRefinements","hierarchicalFacets","facetsExcludes","disjunctiveFacetsRefinements","numericRefinements","tagRefinements","hierarchicalFacetsRefinements"],getQueryParams:function(){var e=this.managedParameters,t={},r=this;return Object.keys(this).forEach((function(n){var i=r[n];-1===e.indexOf(n)&&void 0!==i&&(t[n]=i)})),t},setQueryParameter:function(e,t){if(this[e]===t)return this;var r={};return r[e]=t,this.setQueryParameters(r)},setQueryParameters:function(e){if(!e)return this;var t=m.validate(this,e);if(t)throw t;var r=this,n=m._parseNumbers(e),i=Object.keys(this).reduce((function(e,t){return e[t]=r[t],e}),{}),a=Object.keys(n).reduce((function(e,t){var r=void 0!==e[t],i=void 0!==n[t];return r&&!i?u(e,[t]):(i&&(e[t]=n[t]),e)}),i);return new this.constructor(a)},resetPage:function(){return void 0===this.page?this:this.setPage(0)},_getHierarchicalFacetSortBy:function(e){return e.sortBy||["isRefined:desc","name:asc"]},_getHierarchicalFacetSeparator:function(e){return e.separator||" > "},_getHierarchicalRootPath:function(e){return e.rootPath||null},_getHierarchicalShowParentLevel:function(e){return"boolean"!=typeof e.showParentLevel||e.showParentLevel},getHierarchicalFacetByName:function(e){return i(this.hierarchicalFacets,(function(t){return t.name===e}))},getHierarchicalFacetBreadcrumb:function(e){if(!this.isHierarchicalFacet(e))return[];var t=this.getHierarchicalRefinement(e)[0];if(!t)return[];var r=this._getHierarchicalFacetSeparator(this.getHierarchicalFacetByName(e));return t.split(r).map((function(e){return e.trim()}))},toString:function(){return JSON.stringify(this,null,2)}},e.exports=m},6673:(e,t,r)=>{"use strict";e.exports=function(e){return function(t,r){var n=e.hierarchicalFacets[r],o=e.hierarchicalFacetsRefinements[n.name]&&e.hierarchicalFacetsRefinements[n.name][0]||"",h=e._getHierarchicalFacetSeparator(n),f=e._getHierarchicalRootPath(n),l=e._getHierarchicalShowParentLevel(n),m=a(e._getHierarchicalFacetSortBy(n)),d=t.every((function(e){return e.exhaustive})),p=function(e,t,r,n,a){return function(o,h,f){var l=o;if(f>0){var m=0;for(l=o;m{"use strict";var n=r(8965),i=r(9110),a=r(2909),s=r(849),c=r(3917),u=r(7577),o=r(4728),h=r(8601),f=a.escapeFacetValue,l=a.unescapeFacetValue,m=r(6673);function d(e){var t={};return e.forEach((function(e,r){t[e]=r})),t}function p(e,t,r){t&&t[r]&&(e.stats=t[r])}function v(e,t,r){var a=t[0];this._rawResults=t;var u=this;Object.keys(a).forEach((function(e){u[e]=a[e]}));var h=o({persistHierarchicalRootCount:!1},r);Object.keys(h).forEach((function(e){u[e]=h[e]})),this.processingTimeMS=t.reduce((function(e,t){return void 0===t.processingTimeMS?e:e+t.processingTimeMS}),0),this.disjunctiveFacets=[],this.hierarchicalFacets=e.hierarchicalFacets.map((function(){return[]})),this.facets=[];var f=e.getRefinedDisjunctiveFacets(),v=d(e.facets),g=d(e.disjunctiveFacets),y=1,R=a.facets||{};Object.keys(R).forEach((function(t){var r,n,i=R[t],o=(r=e.hierarchicalFacets,n=t,s(r,(function(e){return(e.attributes||[]).indexOf(n)>-1})));if(o){var h=o.attributes.indexOf(t),f=c(e.hierarchicalFacets,(function(e){return e.name===o.name}));u.hierarchicalFacets[f][h]={attribute:t,data:i,exhaustive:a.exhaustiveFacetsCount}}else{var l,m=-1!==e.disjunctiveFacets.indexOf(t),d=-1!==e.facets.indexOf(t);m&&(l=g[t],u.disjunctiveFacets[l]={name:t,data:i,exhaustive:a.exhaustiveFacetsCount},p(u.disjunctiveFacets[l],a.facets_stats,t)),d&&(l=v[t],u.facets[l]={name:t,data:i,exhaustive:a.exhaustiveFacetsCount},p(u.facets[l],a.facets_stats,t))}})),this.hierarchicalFacets=n(this.hierarchicalFacets),f.forEach((function(r){var n=t[y],s=n&&n.facets?n.facets:{},h=e.getHierarchicalFacetByName(r);Object.keys(s).forEach((function(t){var r,f=s[t];if(h){r=c(e.hierarchicalFacets,(function(e){return e.name===h.name}));var m=c(u.hierarchicalFacets[r],(function(e){return e.attribute===t}));if(-1===m)return;u.hierarchicalFacets[r][m].data=o({},u.hierarchicalFacets[r][m].data,f)}else{r=g[t];var d=a.facets&&a.facets[t]||{};u.disjunctiveFacets[r]={name:t,data:i({},f,d),exhaustive:n.exhaustiveFacetsCount},p(u.disjunctiveFacets[r],n.facets_stats,t),e.disjunctiveFacetsRefinements[t]&&e.disjunctiveFacetsRefinements[t].forEach((function(n){!u.disjunctiveFacets[r].data[n]&&e.disjunctiveFacetsRefinements[t].indexOf(l(n))>-1&&(u.disjunctiveFacets[r].data[n]=0)}))}})),y++})),e.getRefinedHierarchicalFacets().forEach((function(r){var n=e.getHierarchicalFacetByName(r),a=e._getHierarchicalFacetSeparator(n),s=e.getHierarchicalRefinement(r);0===s.length||s[0].split(a).length<2||t.slice(y).forEach((function(t){var r=t&&t.facets?t.facets:{};Object.keys(r).forEach((function(t){var o=r[t],h=c(e.hierarchicalFacets,(function(e){return e.name===n.name})),f=c(u.hierarchicalFacets[h],(function(e){return e.attribute===t}));if(-1!==f){var l={};if(s.length>0&&!u.persistHierarchicalRootCount){var m=s[0].split(a)[0];l[m]=u.hierarchicalFacets[h][f].data[m]}u.hierarchicalFacets[h][f].data=i(l,o,u.hierarchicalFacets[h][f].data)}})),y++}))})),Object.keys(e.facetsExcludes).forEach((function(t){var r=e.facetsExcludes[t],n=v[t];u.facets[n]={name:t,data:R[t],exhaustive:a.exhaustiveFacetsCount},r.forEach((function(e){u.facets[n]=u.facets[n]||{name:t},u.facets[n].data=u.facets[n].data||{},u.facets[n].data[e]=0}))})),this.hierarchicalFacets=this.hierarchicalFacets.map(m(e)),this.facets=n(this.facets),this.disjunctiveFacets=n(this.disjunctiveFacets),this._state=e}function g(e,t){function r(e){return e.name===t}if(e._state.isConjunctiveFacet(t)){var n=s(e.facets,r);return n?Object.keys(n.data).map((function(r){var i=f(r);return{name:r,escapedValue:i,count:n.data[r],isRefined:e._state.isFacetRefined(t,i),isExcluded:e._state.isExcludeRefined(t,r)}})):[]}if(e._state.isDisjunctiveFacet(t)){var i=s(e.disjunctiveFacets,r);return i?Object.keys(i.data).map((function(r){var n=f(r);return{name:r,escapedValue:n,count:i.data[r],isRefined:e._state.isDisjunctiveFacetRefined(t,n)}})):[]}if(e._state.isHierarchicalFacet(t)){var a=s(e.hierarchicalFacets,r);if(!a)return a;var c=e._state.getHierarchicalFacetByName(t),u=e._state._getHierarchicalFacetSeparator(c),o=l(e._state.getHierarchicalRefinement(t)[0]||"");0===o.indexOf(c.rootPath)&&(o=o.replace(c.rootPath+u,""));var h=o.split(u);return h.unshift(t),y(a,h,0),a}}function y(e,t,r){e.isRefined=e.name===(t[r]&&t[r].trim()),e.data&&e.data.forEach((function(e){y(e,t,r+1)}))}function R(e,t,r,n){if(n=n||0,Array.isArray(t))return e(t,r[n]);if(!t.data||0===t.data.length)return t;var a=t.data.map((function(t){return R(e,t,r,n+1)})),s=e(a,r[n]);return 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r=[],n=[],i=(t.order||[]).reduce((function(e,t,r){return e[t]=r,e}),{});e.forEach((function(e){var t=e.path||e.name;void 0!==i[t]?r[i[t]]=e:n.push(e)})),r=r.filter((function(e){return e}));var a,s=t.sortRemainingBy;return"hidden"===s?r:(a="alpha"===s?[["path","name"],["asc","asc"]]:[["count"],["desc"]],r.concat(h(n,a[0],a[1])))}(e,r)}if(Array.isArray(a.sortBy)){var n=u(a.sortBy,v.DEFAULT_SORT);return h(e,n[0],n[1])}if("function"==typeof a.sortBy)return function(e,t){return t.sort(e)}(a.sortBy,e);throw new Error("options.sortBy is optional but if defined it must be either an array of string (predicates) or a sorting function")}),r,n)}},v.prototype.getFacetStats=function(e){return this._state.isConjunctiveFacet(e)?F(this.facets,e):this._state.isDisjunctiveFacet(e)?F(this.disjunctiveFacets,e):void 0},v.prototype.getRefinements=function(){var e=this._state,t=this,r=[];return Object.keys(e.facetsRefinements).forEach((function(n){e.facetsRefinements[n].forEach((function(i){r.push(b(e,"facet",n,i,t.facets))}))})),Object.keys(e.facetsExcludes).forEach((function(n){e.facetsExcludes[n].forEach((function(i){r.push(b(e,"exclude",n,i,t.facets))}))})),Object.keys(e.disjunctiveFacetsRefinements).forEach((function(n){e.disjunctiveFacetsRefinements[n].forEach((function(i){r.push(b(e,"disjunctive",n,i,t.disjunctiveFacets))}))})),Object.keys(e.hierarchicalFacetsRefinements).forEach((function(n){e.hierarchicalFacetsRefinements[n].forEach((function(i){r.push(function(e,t,r,n){var i=e.getHierarchicalFacetByName(t),a=e._getHierarchicalFacetSeparator(i),c=r.split(a),u=s(n,(function(e){return e.name===t})),o=c.reduce((function(e,t){var r=e&&s(e.data,(function(e){return e.name===t}));return void 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m(r,e.results),state:r,_originalResponse:e}}),(function(e){throw i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),e}));this.client.search(n).then((function(e){i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),t(null,new m(r,e.results),r)})).catch((function(e){i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),t(e,null,r)}))},p.prototype.findAnswers=function(e){console.warn("[algoliasearch-helper] answers is no longer supported");var t=this.state,r=this.derivedHelpers[0];if(!r)return Promise.resolve([]);var n=r.getModifiedState(t),i=c({attributesForPrediction:e.attributesForPrediction,nbHits:e.nbHits},{params:o(f._getHitsSearchParams(n),["attributesToSnippet","hitsPerPage","restrictSearchableAttributes","snippetEllipsisText"])}),a="search for answers was called, but this client does not have a function client.initIndex(index).findAnswers";if("function"!=typeof this.client.initIndex)throw new Error(a);var s=this.client.initIndex(n.index);if("function"!=typeof s.findAnswers)throw new Error(a);return s.findAnswers(n.query,e.queryLanguages,i)},p.prototype.searchForFacetValues=function(e,t,r,n){var i="function"==typeof this.client.searchForFacetValues,s="function"==typeof this.client.initIndex;if(!i&&!s&&"function"!=typeof this.client.search)throw new Error("search for facet values (searchable) was called, but this client does not have a function client.searchForFacetValues or client.initIndex(index).searchForFacetValues");var c=this.state.setQueryParameters(n||{}),u=c.isDisjunctiveFacet(e),o=f.getSearchForFacetQuery(e,t,r,c);this._currentNbQueries++;var h,l=this;return i?h=this.client.searchForFacetValues([{indexName:c.index,params:o}]):s?h=this.client.initIndex(c.index).searchForFacetValues(o):(delete o.facetName,h=this.client.search([{type:"facet",facet:e,indexName:c.index,params:o}]).then((function(e){return e.results[0]}))),this.emit("searchForFacetValues",{state:c,facet:e,query:t}),h.then((function(t){return l._currentNbQueries--,0===l._currentNbQueries&&l.emit("searchQueueEmpty"),(t=Array.isArray(t)?t[0]:t).facetHits.forEach((function(t){t.escapedValue=a(t.value),t.isRefined=u?c.isDisjunctiveFacetRefined(e,t.escapedValue):c.isFacetRefined(e,t.escapedValue)})),t}),(function(e){throw l._currentNbQueries--,0===l._currentNbQueries&&l.emit("searchQueueEmpty"),e}))},p.prototype.setQuery=function(e){return this._change({state:this.state.resetPage().setQuery(e),isPageReset:!0}),this},p.prototype.clearRefinements=function(e){return this._change({state:this.state.resetPage().clearRefinements(e),isPageReset:!0}),this},p.prototype.clearTags=function(){return this._change({state:this.state.resetPage().clearTags(),isPageReset:!0}),this},p.prototype.addDisjunctiveFacetRefinement=function(e,t){return this._change({state:this.state.resetPage().addDisjunctiveFacetRefinement(e,t),isPageReset:!0}),this},p.prototype.addDisjunctiveRefine=function(){return this.addDisjunctiveFacetRefinement.apply(this,arguments)},p.prototype.addHierarchicalFacetRefinement=function(e,t){return this._change({state:this.state.resetPage().addHierarchicalFacetRefinement(e,t),isPageReset:!0}),this},p.prototype.addNumericRefinement=function(e,t,r){return this._change({state:this.state.resetPage().addNumericRefinement(e,t,r),isPageReset:!0}),this},p.prototype.addFacetRefinement=function(e,t){return this._change({state:this.state.resetPage().addFacetRefinement(e,t),isPageReset:!0}),this},p.prototype.addRefine=function(){return this.addFacetRefinement.apply(this,arguments)},p.prototype.addFacetExclusion=function(e,t){return this._change({state:this.state.resetPage().addExcludeRefinement(e,t),isPageReset:!0}),this},p.prototype.addExclude=function(){return this.addFacetExclusion.apply(this,arguments)},p.prototype.addTag=function(e){return this._change({state:this.state.resetPage().addTagRefinement(e),isPageReset:!0}),this},p.prototype.removeNumericRefinement=function(e,t,r){return this._change({state:this.state.resetPage().removeNumericRefinement(e,t,r),isPageReset:!0}),this},p.prototype.removeDisjunctiveFacetRefinement=function(e,t){return this._change({state:this.state.resetPage().removeDisjunctiveFacetRefinement(e,t),isPageReset:!0}),this},p.prototype.removeDisjunctiveRefine=function(){return this.removeDisjunctiveFacetRefinement.apply(this,arguments)},p.prototype.removeHierarchicalFacetRefinement=function(e){return this._change({state:this.state.resetPage().removeHierarchicalFacetRefinement(e),isPageReset:!0}),this},p.prototype.removeFacetRefinement=function(e,t){return this._change({state:this.state.resetPage().removeFacetRefinement(e,t),isPageReset:!0}),this},p.prototype.removeRefine=function(){return 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n=e.getModifiedState(t),i=n.index?f._getQueries(n.index,n):[];return r.push({state:n,queriesCount:i.length,helper:e}),e.emit("search",{state:n,results:e.lastResults}),i})),a=Array.prototype.concat.apply(n,i),s=this._queryId++;if(this._currentNbQueries++,!a.length)return Promise.resolve({results:[]}).then(this._dispatchAlgoliaResponse.bind(this,r,s));try{this.client.search(a).then(this._dispatchAlgoliaResponse.bind(this,r,s)).catch(this._dispatchAlgoliaError.bind(this,s))}catch(c){this.emit("error",{error:c})}},p.prototype._dispatchAlgoliaResponse=function(e,t,r){var n=this;if(!(t0},p.prototype._change=function(e){var t=e.state,r=e.isPageReset;t!==this.state&&(this.state=t,this.emit("change",{state:this.state,results:this.lastResults,isPageReset:r}))},p.prototype._recommendChange=function(e){var t=e.state;t!==this.recommendState&&(this.recommendState=t)},p.prototype.clearCache=function(){return 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-

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

As we introduced in our previous blog post [25], the term Maximal Extractable Value (MEV) refers to the highest amount of value that can be extracted from a blockchain by a producer/validator during the process of creating a block. This can be achieved by including, excluding, or rearranging the order of transactions.

MEV is a relatively novel topic with many unexplored security consequences. For instance, on April 3rd, 2023, a malicious validator node operator exploited a vulnerability in the mev-boost-relay [26], the most popular MEV relay, to get access to the transaction bundle content, even when the block was created to fail the validation to be accepted by the beacon chain nodes. With the obtained list of transactions, the attacker could execute a front-running attack, earning 20 million USD from the victim block bundle builders.

The following graphic explains how the normal flow of block creation in Ethereum among validators, builders, and relays should work [27]:

Typically, block proposers (consensus validators) should blindly sign block headers created by block builders and then wait until the blocks are already announced to the network to be able to access the block content (the transactions). Hence, the proposers cannot steal the MEV created by the block builders. However, a vulnerability in the relay software allowed a malicious proposer to modify a block header with corrupt data that the network should not accept.q The relay assumed the signed block as valid and returned the block content to the proposer, which instead extracted the MEV for itself. It then created another valid block that was accepted by the network, therefore stealing the builder's profit.

Here [28], you can see the vulnerability disclosure write-up with a more detailed explanation and countermeasures taken by the developers. One of the mentioned countermeasures is that the relay does not send the block content directly to the block proposer; instead, it has to read it from the network when it's accepted. This approach is only partially practical because a malicious proposer could run many validator nodes or even launch an eclipse attack on the network so it can see the block's content much faster and try to front-run the relay. Tikuna may help detect such eclipse attacks and alert the affected relays.

As we see here, malicious validators are becoming a worrying reality. Monitoring the behavior of validators can help identify those trying to take advantage of the blockchain network. At Sakundi [21], we are developing technologies to monitor the blockchain using AI technologies that may help detect such malicious validators.

In the upcoming series of posts, we will be exploring other topics related to MEV in the context of blockchain technology. Stay tuned for more!

Have questions? Contact us today to find out more about our services.

· 10 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

We are thrilled to announce the completion of the first phase of our Tikuna project! After months of hard work, dedication, and collaboration, we are excited to share the results of the first round of the Ethereum Foundation (EF) academic research grant. We will highlight our project's milestones and examine our team's findings in this blog post.

Overview

Blockchain security is becoming increasingly relevant in today's cyberspace, and it is essential to strengthening each layer's security in its architecture. Our project focuses on the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate with each other and share information. This layer is a vital component of any blockchain, including Ethereum, due to the decentralized nature of its architecture. However, the P2P network layer may be vulnerable to several attacks, such as a Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. Consequently, this layer is still prone to many threats inherited from P2P networks, and there is a need to analyze and understand them by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain network at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Networks (RNNs) for anomaly detection to find attacks on the P2P layer. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including Eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy.

Our project aimed to implement Tikuna, a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. With Tikuna, we aim to enhance the visibility of the status of the P2P network. The project is composed of three primary deliverables as follows:

  1. Development of an open-source P2P monitoring solution accessible to the EF community.
  2. Creation of dashboards to visualize different monitoring aspects of the P2P network.
  3. A draft research paper to present the scientific findings of our Tikuna approach.

Tikuna, the first project in Latin America to get academic research funding from the Ethereum Foundation, has created a novel strategy to increase awareness of the P2P network's security state. We are proud to contribute to advancing blockchain safety and enhancing the Ethereum blockchain's security. To learn more about Tikuna, visit our website [19].

Phase I roadmap

Meaningful findings

We introduce three main contributions:

  • Our proposed machine learning anomaly detection approach can detect several attacks at the Ethereum P2P layer using peer event trace data in a simulation environment using the protocol labs’ framework testground.

  • We demonstrate how to detect eclipse attacks in a mainnet node by extracting custom-generated connection log data from the Ethereum client Prysm and utilizing an LSTM neural network.

  • We developed a custom exploit implementing a real-world eclipse attack. It was tested against a modified Prysm client in mainnet, so it did not forward spam to the network. With this, we could test the effectiveness of our approach.

Our team's initial step was to conduct in-depth research and analysis to gain a thorough understanding of the various existing P2P security approaches, i.e., state of the art techniques. Based on this, we developed a comprehensive plan of action outlining our Tikuna approach and methodology.

Subsequently, we proceeded to implement an LSTM based approach for Ethereum blockchain network security monitoring and anomaly detection. Additionally, our team developed and implemented three dashboards aimed at enhancing P2P security. These dashboards are the Ethereum P2P Network Status dashboard, the Beacon Node Monitor, and the Eclipse Attacks Dashboard.

Finally, throughout the development process, we rigorously tested our approach to ensure that it was functional. We also conducted several experiments in two distinct network environments, i.e., the simulated testground and the Ethereum mainnet, to thoroughly evaluate the effectiveness as well as the performance of the Tikuna approach.

The following is a sample of the training data used for Tikuna in the Ethereum mainnet, consisting of discovery connection data (UDP) from honest and simulated attacking peers. The normal data was collected from several nodes during 3 days under regular operation, and the malicious data was gathered from a single victim Ethereum node, using our developed exploit. Each line has several input features, including timestamp, IP, and Port removed from the peer table, IP and Port added to the peer table, and bucket where the peer is added.

The model and simulations

We have used RNNs for our research. They are a type of model frequently utilized for processing sequential data such as time series. These models are specialized for processing a sequence of values that are a function of time. RNNs can scale to long sequences that would not be practical for networks without sequence-based specialization. Most recurrent networks can also process sequences of variable length.

One of these models is especially of interest to this research. The long short-term memory model (LSTM) uses a gating mechanism to ensure proper information propagation through many time steps. LSTM networks have a specific memory cell and can capture long-term dependencies in sequential data. LSTM are valuable tools for language modeling problems. LSTM networks are a version of recurrent neural networks useful for long interrelated sequences of data. LSTM was chosen in this research for anomaly detection to find malicious discovery connections to an Ethereum client.

Following there is an example of the ML model output for mainnet where we obtained the best results detecting eclipse attacks, with 1’000,000 regular connection log lines for training and around 3,000 lines of eclipse attack logs for evaluation:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

We used standard measures metrics for intrusion detection with ML, such as f1, recall, precision, and accuracy. Please review the Github repository and stay tuned for our research paper publication if you want more details.

Dashboards

As part of our research, our team considered it important to demonstrate the results we obtained as well as a possible implementation. To achieve this, we created three Grafana dashboards that are integrated with Prometheus. These dashboards capture monitoring metrics and display the behavior of the P2P network, neighborhood, and Prysm node in real-time.

  • Ethereum P2P Network Status: The information that is displayed on the Ethereum P2P Network Status dashboard comes from our Prysm node and is obtained by monitoring the port 8080. It gives data on the neighborhood area as well as how this node interacts with the other nodes in the blockchain. This comprises the number of connected peers, the current and head slot, the various types of P2P libraries that are linked to our node together with their ratings, as well as information on the participation rate and network liveness of the P2P network. In addition to this, it provides information concerning validators, such as their total numbers, the status of their accounts, and the average amount of ether in their balances.

Fig. 1 The Ethereum P2P network status dashboard

  • Beacon Node Monitor: This dashboard belongs to the infrastructure of our own Ethereum node, which displays information such as the use of CPU, RAM, disk space, bandwidth within the network, and details about storage space. This information was retrieved from the Kubernetes cluster that serves as the host for the consensus node, which is the infrastructure in our particular instance.

Fig. 2 The beacon node monitor dashboard

  • Eclipse Attacks Dashboard: The creation of the final dashboard required the development of an interface that could record the potential eclipse attacks discovered by our machine learning algorithm and then input those records into the AlertManager component of Prometheus. After that, Grafana creates a table that displays a row for each identified eclipse attack in a given node.

Fig. 3 The eclipse attacks dashboard

At Tikuna, we believe that making a more secure and reliable P2P network requires accessibility and transparency, which is why we have developed a comprehensive User section for our proof-of-concept. Our User section offers detailed instructions and tutorials that guide users of all technical levels through the installation process and explain how to interpret the information presented in the dashboards. With Tikuna, a wider audience can benefit from our P2P monitoring system. We are proud to contribute to advancing blockchain security through our innovative ideas and commitment to user-friendly solutions. To learn more about Tikuna and how to use our monitoring system, visit our website [19] and browse through our User section.

The research paper

We are currently working on a draft research paper that is based on the findings and results obtained during this research grant. Once we receive feedback from EF, we intend to submit the research paper to an International Conference. The research paper is organized into five main chapters. +

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

As we introduced in our previous blog post [25], the term Maximal Extractable Value (MEV) refers to the highest amount of value that can be extracted from a blockchain by a producer/validator during the process of creating a block. This can be achieved by including, excluding, or rearranging the order of transactions.

MEV is a relatively novel topic with many unexplored security consequences. For instance, on April 3rd, 2023, a malicious validator node operator exploited a vulnerability in the mev-boost-relay [26], the most popular MEV relay, to get access to the transaction bundle content, even when the block was created to fail the validation to be accepted by the beacon chain nodes. With the obtained list of transactions, the attacker could execute a front-running attack, earning 20 million USD from the victim block bundle builders.

The following graphic explains how the normal flow of block creation in Ethereum among validators, builders, and relays should work [27]:

Typically, block proposers (consensus validators) should blindly sign block headers created by block builders and then wait until the blocks are already announced to the network to be able to access the block content (the transactions). Hence, the proposers cannot steal the MEV created by the block builders. However, a vulnerability in the relay software allowed a malicious proposer to modify a block header with corrupt data that the network should not accept.q The relay assumed the signed block as valid and returned the block content to the proposer, which instead extracted the MEV for itself. It then created another valid block that was accepted by the network, therefore stealing the builder's profit.

Here [28], you can see the vulnerability disclosure write-up with a more detailed explanation and countermeasures taken by the developers. One of the mentioned countermeasures is that the relay does not send the block content directly to the block proposer; instead, it has to read it from the network when it's accepted. This approach is only partially practical because a malicious proposer could run many validator nodes or even launch an eclipse attack on the network so it can see the block's content much faster and try to front-run the relay. Tikuna may help detect such eclipse attacks and alert the affected relays.

As we see here, malicious validators are becoming a worrying reality. Monitoring the behavior of validators can help identify those trying to take advantage of the blockchain network. At Sakundi [21], we are developing technologies to monitor the blockchain using AI technologies that may help detect such malicious validators.

In the upcoming series of posts, we will be exploring other topics related to MEV in the context of blockchain technology. Stay tuned for more!

Have questions? Contact us today to find out more about our services.

· 10 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

We are thrilled to announce the completion of the first phase of our Tikuna project! After months of hard work, dedication, and collaboration, we are excited to share the results of the first round of the Ethereum Foundation (EF) academic research grant. We will highlight our project's milestones and examine our team's findings in this blog post.

Overview

Blockchain security is becoming increasingly relevant in today's cyberspace, and it is essential to strengthening each layer's security in its architecture. Our project focuses on the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate with each other and share information. This layer is a vital component of any blockchain, including Ethereum, due to the decentralized nature of its architecture. However, the P2P network layer may be vulnerable to several attacks, such as a Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. Consequently, this layer is still prone to many threats inherited from P2P networks, and there is a need to analyze and understand them by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain network at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Networks (RNNs) for anomaly detection to find attacks on the P2P layer. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including Eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy.

Our project aimed to implement Tikuna, a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. With Tikuna, we aim to enhance the visibility of the status of the P2P network. The project is composed of three primary deliverables as follows:

  1. Development of an open-source P2P monitoring solution accessible to the EF community.
  2. Creation of dashboards to visualize different monitoring aspects of the P2P network.
  3. A draft research paper to present the scientific findings of our Tikuna approach.

Tikuna, the first project in Latin America to get academic research funding from the Ethereum Foundation, has created a novel strategy to increase awareness of the P2P network's security state. We are proud to contribute to advancing blockchain safety and enhancing the Ethereum blockchain's security. To learn more about Tikuna, visit our website [19].

Phase I roadmap

Meaningful findings

We introduce three main contributions:

  • Our proposed machine learning anomaly detection approach can detect several attacks at the Ethereum P2P layer using peer event trace data in a simulation environment using the protocol labs’ framework testground.

  • We demonstrate how to detect eclipse attacks in a mainnet node by extracting custom-generated connection log data from the Ethereum client Prysm and utilizing an LSTM neural network.

  • We developed a custom exploit implementing a real-world eclipse attack. It was tested against a modified Prysm client in mainnet, so it did not forward spam to the network. With this, we could test the effectiveness of our approach.

Our team's initial step was to conduct in-depth research and analysis to gain a thorough understanding of the various existing P2P security approaches, i.e., state of the art techniques. Based on this, we developed a comprehensive plan of action outlining our Tikuna approach and methodology.

Subsequently, we proceeded to implement an LSTM based approach for Ethereum blockchain network security monitoring and anomaly detection. Additionally, our team developed and implemented three dashboards aimed at enhancing P2P security. These dashboards are the Ethereum P2P Network Status dashboard, the Beacon Node Monitor, and the Eclipse Attacks Dashboard.

Finally, throughout the development process, we rigorously tested our approach to ensure that it was functional. We also conducted several experiments in two distinct network environments, i.e., the simulated testground and the Ethereum mainnet, to thoroughly evaluate the effectiveness as well as the performance of the Tikuna approach.

The following is a sample of the training data used for Tikuna in the Ethereum mainnet, consisting of discovery connection data (UDP) from honest and simulated attacking peers. The normal data was collected from several nodes during 3 days under regular operation, and the malicious data was gathered from a single victim Ethereum node, using our developed exploit. Each line has several input features, including timestamp, IP, and Port removed from the peer table, IP and Port added to the peer table, and bucket where the peer is added.

The model and simulations

We have used RNNs for our research. They are a type of model frequently utilized for processing sequential data such as time series. These models are specialized for processing a sequence of values that are a function of time. RNNs can scale to long sequences that would not be practical for networks without sequence-based specialization. Most recurrent networks can also process sequences of variable length.

One of these models is especially of interest to this research. The long short-term memory model (LSTM) uses a gating mechanism to ensure proper information propagation through many time steps. LSTM networks have a specific memory cell and can capture long-term dependencies in sequential data. LSTM are valuable tools for language modeling problems. LSTM networks are a version of recurrent neural networks useful for long interrelated sequences of data. LSTM was chosen in this research for anomaly detection to find malicious discovery connections to an Ethereum client.

Following there is an example of the ML model output for mainnet where we obtained the best results detecting eclipse attacks, with 1’000,000 regular connection log lines for training and around 3,000 lines of eclipse attack logs for evaluation:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

We used standard measures metrics for intrusion detection with ML, such as f1, recall, precision, and accuracy. Please review the Github repository and stay tuned for our research paper publication if you want more details.

Dashboards

As part of our research, our team considered it important to demonstrate the results we obtained as well as a possible implementation. To achieve this, we created three Grafana dashboards that are integrated with Prometheus. These dashboards capture monitoring metrics and display the behavior of the P2P network, neighborhood, and Prysm node in real-time.

  • Ethereum P2P Network Status: The information that is displayed on the Ethereum P2P Network Status dashboard comes from our Prysm node and is obtained by monitoring the port 8080. It gives data on the neighborhood area as well as how this node interacts with the other nodes in the blockchain. This comprises the number of connected peers, the current and head slot, the various types of P2P libraries that are linked to our node together with their ratings, as well as information on the participation rate and network liveness of the P2P network. In addition to this, it provides information concerning validators, such as their total numbers, the status of their accounts, and the average amount of ether in their balances.

Fig. 1 The Ethereum P2P network status dashboard

  • Beacon Node Monitor: This dashboard belongs to the infrastructure of our own Ethereum node, which displays information such as the use of CPU, RAM, disk space, bandwidth within the network, and details about storage space. This information was retrieved from the Kubernetes cluster that serves as the host for the consensus node, which is the infrastructure in our particular instance.

Fig. 2 The beacon node monitor dashboard

  • Eclipse Attacks Dashboard: The creation of the final dashboard required the development of an interface that could record the potential eclipse attacks discovered by our machine learning algorithm and then input those records into the AlertManager component of Prometheus. After that, Grafana creates a table that displays a row for each identified eclipse attack in a given node.

Fig. 3 The eclipse attacks dashboard

At Tikuna, we believe that making a more secure and reliable P2P network requires accessibility and transparency, which is why we have developed a comprehensive User section for our proof-of-concept. Our User section offers detailed instructions and tutorials that guide users of all technical levels through the installation process and explain how to interpret the information presented in the dashboards. With Tikuna, a wider audience can benefit from our P2P monitoring system. We are proud to contribute to advancing blockchain security through our innovative ideas and commitment to user-friendly solutions. To learn more about Tikuna and how to use our monitoring system, visit our website [19] and browse through our User section.

The research paper

We are currently working on a draft research paper that is based on the findings and results obtained during this research grant. Once we receive feedback from EF, we intend to submit the research paper to an International Conference. The research paper is organized into five main chapters. The first chapter is an introduction to the Ethereum blockchain, which emphasizes the importance of understanding the risks associated with P2P blockchain networks and developing security-focused solutions to ensure their reliability. It also highlights the three main contributions of the paper, which include a proposed machine learning approach for detecting attacks in the P2P layer, a method for detecting eclipse attacks using custom-generated connection log data and LSTM neural networks, and a custom exploit for eclipse attack testing against a modified Prysm client in mainnet. The second chapter, titled "Related Work", provides an overview of the most recent works that address the security challenges of the Ethereum blockchain P2P networks. The third chapter introduces the Tikuna approach by describing each of the three steps and discussing the various types of blockchain P2P network attacks. The fourth chapter evaluates the effectiveness of the Tikuna approach using a simulation and mainnet connection dataset. The last chapter presents a summary of the proposed work, conclusions, and potential future research directions. Finally, the authors acknowledge that the work presented was supported by the Ethereum Foundation Academic Research Grants.

Next steps

We take immense pride in our team's accomplishments, and we extend our gratitude to everyone who played a part in ensuring the project's success. Our team's expertise, dedication, and collaborative ethos were crucial to delivering the first phase of the Tikuna project. This is just the beginning of our project as we aim to further develop Tikuna as a tool to contribute to the Ethereum community and other Blockchains. Our efforts will be focused on identifying additional attacks, minimizing false positives, detecting real-world incidents, and incorporating different Blockchain (including Ethereum) node clients. Moreover, we intend to investigate other areas of research where Tikuna can be utilized, such as Maximal Extractable Value (MEV).

We have applied for the second round of the EF academic research grants. Please see our application here. If you are interested in learning more about Tikuna or working with us, we welcome the opportunity to collaborate with you. Kindly reach out to us, and we'll be happy to hear from you.

Here you can find the Tikuna's full source code.

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Although there are advances in cybersecurity, many applications need robust security features. Currently, aside from product quality concerns, the software has numerous adversaries that are attempting to break the lines in order to exploit or sabotage its conditions.

The situation is similar to that of Web 3, and it may even be more severe due to the involvement of factors such as digital assets (crypto or NFTs). Even if the blockchain and its consensus algorithm are used as the basis for carrying out the backend procedures of these technologies, they still have dozens of security vulnerabilities that are found over time by hackers whose objective is not to contribute to the ecosystem. Imagine a large, decentralized network in which hundreds of innovative or newly emerging projects are competing to use it as a platform for the development of their apps. This is a real-world occurrence that takes place on the Ethereum blockchain. As of right now, the increasing levels of hacks, chasing funds, or oversights by institutions, such as the most recent FTX issue, have positioned blockchain cybersecurity as the shelter behind the perfection of its operations. Exchanges, smart contracts, tokens, and decentralized applications are all components of the system that are subjected to quality and security verification as part of the auditing process. Hacks to decentralized finance protocols resulted in the theft of 156 million dollars in just four months during the year 2021. Aside from the fact that this amount represents a significant loss for both individuals and businesses, it is also a sign on a qualitative level, indicating that protocols require integration and maintenance with regard to computer security.

This is something that the Tikuna team is fully aware of. Therefore, we are trying to make an essential contribution to the blockchain community as well as the field of cybersecurity via the development and provision of techniques that help with the early detection/monitoring of threats for the Ethereum P2P network. Moreover, the presence of systems that can enforce and monitor decentralized environments before the occurrence of these unfavorable events instills a greater sense of confidence and intense anticipatory ownership. As an open-source solution, users will have the ability to use the data that is provided in order to improve the integration of security measures. Finally, we are working hard to make the use of blockchain products more secure and predictable, which are both highly valued in the digital world.

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Cybersecurity systems are complex and play a fundamental role in various applications. Even in highly secure and decentralized systems like blockchain networks, there are cases in which the dApps, average users, or the network itself are exposed to many daily risks. Due to certain vulnerabilities, numerous businesses are interested in fostering research and development of security-focused solutions, which is precisely why our team is developing Tikuna.

Tikuna is a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. Using Machine Learning techniques, it will extract security and performance insights for detecting relevant incidents and improving visibility to the security state of the P2P network. This proposed research project has been approved and supported by the Ethereum Foundation with an award of USD 102,200 to start the development process.

Tikuna team has approached this 2022 opening in the area of P2P Network to the Ethereum Foundation Academic Grants. We are the only team in Latin America that received approval and funding from ETH Foundation. This is the official announcement published by them.

The team behind Tikuna

edenia logo

The project Tikuna is led by both Edenia and Sakundi. Edenia operates an independent decentralized blockchain infrastructure and develops Web3 products. Sakundi is a blockchain security and privacy-focused research organization that provides Artificial Intelligence-powered security monitoring solutions for blockchain businesses and distributed apps.

What is the problem that Tikuna is trying to solve?

Tikuna proposes an open-source solution that provides visibility into the nodes’ status, allowing for the early detection of P2P attacks. This tool will provide the ETH Community with an extra resource for preventing or reporting incidents that could affect thousands of transactions and/or applications running on this blockchain.

What will be the final research project deliverable of Tikuna, in this first stage?

The project aims to empower the Ethereum community by providing them with an intelligent, state-of-the-art tool capable of accumulating security-related data from the state of the P2P network and enhancing network visibility by offering insights about its current state. A handy feature we plan to develop is an interactive dashboard that allows users or organizations to make queries of the nodes.

Steps toward the future….

Tikuna is an innovative solution that will support Ethereum's blockchain security, more so, in the evolution to PoS (Proof-of-Stake), where nodes play a bigger role in block validation with their participation. With how fast Web3 expands and how many applications use participating networks, we reckon that Tikuna has the potential to become a multi-chain solution. We plan to further expand Tikuna to different networks that use PoS or DPoS as their consensus mechanism.

- - +
+ + \ No newline at end of file diff --git a/blog/introducing-tikuna/index.html b/blog/introducing-tikuna/index.html index a56407f..2987225 100644 --- a/blog/introducing-tikuna/index.html +++ b/blog/introducing-tikuna/index.html @@ -3,24 +3,20 @@ -Tikuna: Blockchain Security Using AI - Supported by the Ethereum Foundation. | Blockchain Security Monitoring - - - - - - - +Tikuna: Blockchain Security Using AI - Supported by the Ethereum Foundation. | Blockchain Security Monitoring + + +
-

Tikuna: Blockchain Security Using AI - Supported by the Ethereum Foundation.

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Cybersecurity systems are complex and play a fundamental role in various applications. Even in highly secure and decentralized systems like blockchain networks, there are cases in which the dApps, average users, or the network itself are exposed to many daily risks. Due to certain vulnerabilities, numerous businesses are interested in fostering research and development of security-focused solutions, which is precisely why our team is developing Tikuna.

Tikuna is a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. Using Machine Learning techniques, it will extract security and performance insights for detecting relevant incidents and improving visibility to the security state of the P2P network. This proposed research project has been approved and supported by the Ethereum Foundation with an award of USD 102,200 to start the development process.

Tikuna team has approached this 2022 opening in the area of P2P Network to the Ethereum Foundation Academic Grants. We are the only team in Latin America that received approval and funding from ETH Foundation. This is the official announcement published by them.

The team behind Tikuna

edenia logo

The project Tikuna is led by both Edenia and Sakundi. Edenia operates an independent decentralized blockchain infrastructure and develops Web3 products. Sakundi is a blockchain security and privacy-focused research organization that provides Artificial Intelligence-powered security monitoring solutions for blockchain businesses and distributed apps.

What is the problem that Tikuna is trying to solve?

Tikuna proposes an open-source solution that provides visibility into the nodes’ status, allowing for the early detection of P2P attacks. This tool will provide the ETH Community with an extra resource for preventing or reporting incidents that could affect thousands of transactions and/or applications running on this blockchain.

What will be the final research project deliverable of Tikuna, in this first stage?

The project aims to empower the Ethereum community by providing them with an intelligent, state-of-the-art tool capable of accumulating security-related data from the state of the P2P network and enhancing network visibility by offering insights about its current state. +

Tikuna: Blockchain Security Using AI - Supported by the Ethereum Foundation.

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Cybersecurity systems are complex and play a fundamental role in various applications. Even in highly secure and decentralized systems like blockchain networks, there are cases in which the dApps, average users, or the network itself are exposed to many daily risks. Due to certain vulnerabilities, numerous businesses are interested in fostering research and development of security-focused solutions, which is precisely why our team is developing Tikuna.

Tikuna is a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. Using Machine Learning techniques, it will extract security and performance insights for detecting relevant incidents and improving visibility to the security state of the P2P network. This proposed research project has been approved and supported by the Ethereum Foundation with an award of USD 102,200 to start the development process.

Tikuna team has approached this 2022 opening in the area of P2P Network to the Ethereum Foundation Academic Grants. We are the only team in Latin America that received approval and funding from ETH Foundation. This is the official announcement published by them.

The team behind Tikuna

edenia logo

The project Tikuna is led by both Edenia and Sakundi. Edenia operates an independent decentralized blockchain infrastructure and develops Web3 products. Sakundi is a blockchain security and privacy-focused research organization that provides Artificial Intelligence-powered security monitoring solutions for blockchain businesses and distributed apps.

What is the problem that Tikuna is trying to solve?

Tikuna proposes an open-source solution that provides visibility into the nodes’ status, allowing for the early detection of P2P attacks. This tool will provide the ETH Community with an extra resource for preventing or reporting incidents that could affect thousands of transactions and/or applications running on this blockchain.

What will be the final research project deliverable of Tikuna, in this first stage?

The project aims to empower the Ethereum community by providing them with an intelligent, state-of-the-art tool capable of accumulating security-related data from the state of the P2P network and enhancing network visibility by offering insights about its current state. A handy feature we plan to develop is an interactive dashboard that allows users or organizations to make queries of the nodes.

Steps toward the future….

Tikuna is an innovative solution that will support Ethereum's blockchain security, more so, in the evolution to PoS (Proof-of-Stake), where nodes play a bigger role in block validation with their participation. With how fast Web3 expands and how many applications use participating networks, we reckon that Tikuna has the potential to become a multi-chain solution. We plan to further expand Tikuna to different networks that use PoS or DPoS as their consensus mechanism.



- - +
+ + \ No newline at end of file diff --git a/blog/malicious-validators/index.html b/blog/malicious-validators/index.html index cb6d471..ccdcc83 100644 --- a/blog/malicious-validators/index.html +++ b/blog/malicious-validators/index.html @@ -3,23 +3,19 @@ -The Danger of Malicious Block Proposers in Maximal Extractable Value | Blockchain Security Monitoring - - - - - - - +The Danger of Malicious Block Proposers in Maximal Extractable Value | Blockchain Security Monitoring + + +
-

The Danger of Malicious Block Proposers in Maximal Extractable Value

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

As we introduced in our previous blog post [25], the term Maximal Extractable Value (MEV) refers to the highest amount of value that can be extracted from a blockchain by a producer/validator during the process of creating a block. This can be achieved by including, excluding, or rearranging the order of transactions.

MEV is a relatively novel topic with many unexplored security consequences. For instance, on April 3rd, 2023, a malicious validator node operator exploited a vulnerability in the mev-boost-relay [26], the most popular MEV relay, to get access to the transaction bundle content, even when the block was created to fail the validation to be accepted by the beacon chain nodes. With the obtained list of transactions, the attacker could execute a front-running attack, earning 20 million USD from the victim block bundle builders.

The following graphic explains how the normal flow of block creation in Ethereum among validators, builders, and relays should work [27]:

Typically, block proposers (consensus validators) should blindly sign block headers created by block builders and then wait until the blocks are already announced to the network to be able to access the block content (the transactions). Hence, the proposers cannot steal the MEV created by the block builders. However, a vulnerability in the relay software allowed a malicious proposer to modify a block header with corrupt data that the network should not accept.q The relay assumed the signed block as valid and returned the block content to the proposer, which instead extracted the MEV for itself. It then created another valid block that was accepted by the network, therefore stealing the builder's profit.

Here [28], you can see the vulnerability disclosure write-up with a more detailed explanation and countermeasures taken by the developers. One of the mentioned countermeasures is that the relay does not send the block content directly to the block proposer; instead, it has to read it from the network when it's accepted. This approach is only partially practical because a malicious proposer could run many validator nodes or even launch an eclipse attack on the network so it can see the block's content much faster and try to front-run the relay. Tikuna may help detect such eclipse attacks and alert the affected relays.

As we see here, malicious validators are becoming a worrying reality. Monitoring the behavior of validators can help identify those trying to take advantage of the blockchain network. At Sakundi [21], we are developing technologies to monitor the blockchain using AI technologies that may help detect such malicious validators.

In the upcoming series of posts, we will be exploring other topics related to MEV in the context of blockchain technology. Stay tuned for more!

Have questions? Contact us today to find out more about our services.



+ + \ No newline at end of file diff --git a/blog/tikuna-contribution/index.html b/blog/tikuna-contribution/index.html index 8c230dc..cf5e807 100644 --- a/blog/tikuna-contribution/index.html +++ b/blog/tikuna-contribution/index.html @@ -3,23 +3,19 @@ -Tikuna - Our contribution to security on the blockchain | Blockchain Security Monitoring - - - - - - - +Tikuna - Our contribution to security on the blockchain | Blockchain Security Monitoring + + +
-

Tikuna - Our contribution to security on the blockchain

· 3 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Although there are advances in cybersecurity, many applications need robust security features. Currently, aside from product quality concerns, the software has numerous adversaries that are attempting to break the lines in order to exploit or sabotage its conditions.

The situation is similar to that of Web 3, and it may even be more severe due to the involvement of factors such as digital assets (crypto or NFTs). Even if the blockchain and its consensus algorithm are used as the basis for carrying out the backend procedures of these technologies, they still have dozens of security vulnerabilities that are found over time by hackers whose objective is not to contribute to the ecosystem. Imagine a large, decentralized network in which hundreds of innovative or newly emerging projects are competing to use it as a platform for the development of their apps. This is a real-world occurrence that takes place on the Ethereum blockchain. As of right now, the increasing levels of hacks, chasing funds, or oversights by institutions, such as the most recent FTX issue, have positioned blockchain cybersecurity as the shelter behind the perfection of its operations. Exchanges, smart contracts, tokens, and decentralized applications are all components of the system that are subjected to quality and security verification as part of the auditing process. Hacks to decentralized finance protocols resulted in the theft of 156 million dollars in just four months during the year 2021. Aside from the fact that this amount represents a significant loss for both individuals and businesses, it is also a sign on a qualitative level, indicating that protocols require integration and maintenance with regard to computer security.

This is something that the Tikuna team is fully aware of. Therefore, we are trying to make an essential contribution to the blockchain community as well as the field of cybersecurity via the development and provision of techniques that help with the early detection/monitoring of threats for the Ethereum P2P network. Moreover, the presence of systems that can enforce and monitor decentralized environments before the occurrence of these unfavorable events instills a greater sense of confidence and intense anticipatory ownership. As an open-source solution, users will have the ability to use the data that is provided in order to improve the integration of security measures. Finally, we are working hard to make the use of blockchain products more secure and predictable, which are both highly valued in the digital world.



+ + \ No newline at end of file diff --git a/blog/tikuna-final-blogpost-fase1/index.html b/blog/tikuna-final-blogpost-fase1/index.html index 0565319..6c40670 100644 --- a/blog/tikuna-final-blogpost-fase1/index.html +++ b/blog/tikuna-final-blogpost-fase1/index.html @@ -3,25 +3,21 @@ -Marking the Delivery of our Tikuna Project Proof-Of-Concept Phase | Blockchain Security Monitoring - - - - - - - +Marking the Delivery of our Tikuna Project Proof-Of-Concept Phase | Blockchain Security Monitoring + + +
-

Marking the Delivery of our Tikuna Project Proof-Of-Concept Phase

· 10 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

We are thrilled to announce the completion of the first phase of our Tikuna project! After months of hard work, dedication, and collaboration, we are excited to share the results of the first round of the Ethereum Foundation (EF) academic research grant. We will highlight our project's milestones and examine our team's findings in this blog post.

Overview

Blockchain security is becoming increasingly relevant in today's cyberspace, and it is essential to strengthening each layer's security in its architecture. Our project focuses on the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate with each other and share information. This layer is a vital component of any blockchain, including Ethereum, due to the decentralized nature of its architecture. However, the P2P network layer may be vulnerable to several attacks, such as a Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. Consequently, this layer is still prone to many threats inherited from P2P networks, and there is a need to analyze and understand them by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain network at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Networks (RNNs) for anomaly detection to find attacks on the P2P layer. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including Eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy.

Our project aimed to implement Tikuna, a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. With Tikuna, we aim to enhance the visibility of the status of the P2P network. The project is composed of three primary deliverables as follows:

  1. Development of an open-source P2P monitoring solution accessible to the EF community.
  2. Creation of dashboards to visualize different monitoring aspects of the P2P network.
  3. A draft research paper to present the scientific findings of our Tikuna approach.

Tikuna, the first project in Latin America to get academic research funding from the Ethereum Foundation, has created a novel strategy to increase awareness of the P2P network's security state. We are proud to contribute to advancing blockchain safety and enhancing the Ethereum blockchain's security. To learn more about Tikuna, visit our website [19].

Phase I roadmap

Meaningful findings

We introduce three main contributions:

  • Our proposed machine learning anomaly detection approach can detect several attacks at the Ethereum P2P layer using peer event trace data in a simulation environment using the protocol labs’ framework testground.

  • We demonstrate how to detect eclipse attacks in a mainnet node by extracting custom-generated connection log data from the Ethereum client Prysm and utilizing an LSTM neural network.

  • We developed a custom exploit implementing a real-world eclipse attack. It was tested against a modified Prysm client in mainnet, so it did not forward spam to the network. With this, we could test the effectiveness of our approach.

Our team's initial step was to conduct in-depth research and analysis to gain a thorough understanding of the various existing P2P security approaches, i.e., state of the art techniques. Based on this, we developed a comprehensive plan of action outlining our Tikuna approach and methodology.

Subsequently, we proceeded to implement an LSTM based approach for Ethereum blockchain network security monitoring and anomaly detection. Additionally, our team developed and implemented three dashboards aimed at enhancing P2P security. These dashboards are the Ethereum P2P Network Status dashboard, the Beacon Node Monitor, and the Eclipse Attacks Dashboard.

Finally, throughout the development process, we rigorously tested our approach to ensure that it was functional. We also conducted several experiments in two distinct network environments, i.e., the simulated testground and the Ethereum mainnet, to thoroughly evaluate the effectiveness as well as the performance of the Tikuna approach.

The following is a sample of the training data used for Tikuna in the Ethereum mainnet, consisting of discovery connection data (UDP) from honest and simulated attacking peers. The normal data was collected from several nodes during 3 days under regular operation, and the malicious data was gathered from a single victim Ethereum node, using our developed exploit. Each line has several input features, including timestamp, IP, and Port removed from the peer table, IP and Port added to the peer table, and bucket where the peer is added.

The model and simulations

We have used RNNs for our research. They are a type of model frequently utilized for processing sequential data such as time series. These models are specialized for processing a sequence of values that are a function of time. RNNs can scale to long sequences that would not be practical for networks without sequence-based specialization. Most recurrent networks can also process sequences of variable length.

One of these models is especially of interest to this research. The long short-term memory model (LSTM) uses a gating mechanism to ensure proper information propagation through many time steps. LSTM networks have a specific memory cell and can capture long-term dependencies in sequential data. LSTM are valuable tools for language modeling problems. LSTM networks are a version of recurrent neural networks useful for long interrelated sequences of data. LSTM was chosen in this research for anomaly detection to find malicious discovery connections to an Ethereum client.

Following there is an example of the ML model output for mainnet where we obtained the best results detecting eclipse attacks, with 1’000,000 regular connection log lines for training and around 3,000 lines of eclipse attack logs for evaluation:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

We used standard measures metrics for intrusion detection with ML, such as f1, recall, precision, and accuracy. Please review the Github repository and stay tuned for our research paper publication if you want more details.

Dashboards

As part of our research, our team considered it important to demonstrate the results we obtained as well as a possible implementation. To achieve this, we created three Grafana dashboards that are integrated with Prometheus. These dashboards capture monitoring metrics and display the behavior of the P2P network, neighborhood, and Prysm node in real-time.

  • Ethereum P2P Network Status: The information that is displayed on the Ethereum P2P Network Status dashboard comes from our Prysm node and is obtained by monitoring the port 8080. It gives data on the neighborhood area as well as how this node interacts with the other nodes in the blockchain. This comprises the number of connected peers, the current and head slot, the various types of P2P libraries that are linked to our node together with their ratings, as well as information on the participation rate and network liveness of the P2P network. In addition to this, it provides information concerning validators, such as their total numbers, the status of their accounts, and the average amount of ether in their balances.

Fig. 1 The Ethereum P2P network status dashboard

  • Beacon Node Monitor: This dashboard belongs to the infrastructure of our own Ethereum node, which displays information such as the use of CPU, RAM, disk space, bandwidth within the network, and details about storage space. This information was retrieved from the Kubernetes cluster that serves as the host for the consensus node, which is the infrastructure in our particular instance.

Fig. 2 The beacon node monitor dashboard

  • Eclipse Attacks Dashboard: The creation of the final dashboard required the development of an interface that could record the potential eclipse attacks discovered by our machine learning algorithm and then input those records into the AlertManager component of Prometheus. After that, Grafana creates a table that displays a row for each identified eclipse attack in a given node.

Fig. 3 The eclipse attacks dashboard

At Tikuna, we believe that making a more secure and reliable P2P network requires accessibility and transparency, which is why we have developed a comprehensive User section for our proof-of-concept. Our User section offers detailed instructions and tutorials that guide users of all technical levels through the installation process and explain how to interpret the information presented in the dashboards. With Tikuna, a wider audience can benefit from our P2P monitoring system. We are proud to contribute to advancing blockchain security through our innovative ideas and commitment to user-friendly solutions. To learn more about Tikuna and how to use our monitoring system, visit our website [19] and browse through our User section.

The research paper

We are currently working on a draft research paper that is based on the findings and results obtained during this research grant. Once we receive feedback from EF, we intend to submit the research paper to an International Conference. The research paper is organized into five main chapters. +

Marking the Delivery of our Tikuna Project Proof-Of-Concept Phase

· 10 min read
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

We are thrilled to announce the completion of the first phase of our Tikuna project! After months of hard work, dedication, and collaboration, we are excited to share the results of the first round of the Ethereum Foundation (EF) academic research grant. We will highlight our project's milestones and examine our team's findings in this blog post.

Overview

Blockchain security is becoming increasingly relevant in today's cyberspace, and it is essential to strengthening each layer's security in its architecture. Our project focuses on the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate with each other and share information. This layer is a vital component of any blockchain, including Ethereum, due to the decentralized nature of its architecture. However, the P2P network layer may be vulnerable to several attacks, such as a Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. Consequently, this layer is still prone to many threats inherited from P2P networks, and there is a need to analyze and understand them by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain network at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Networks (RNNs) for anomaly detection to find attacks on the P2P layer. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including Eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy.

Our project aimed to implement Tikuna, a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. With Tikuna, we aim to enhance the visibility of the status of the P2P network. The project is composed of three primary deliverables as follows:

  1. Development of an open-source P2P monitoring solution accessible to the EF community.
  2. Creation of dashboards to visualize different monitoring aspects of the P2P network.
  3. A draft research paper to present the scientific findings of our Tikuna approach.

Tikuna, the first project in Latin America to get academic research funding from the Ethereum Foundation, has created a novel strategy to increase awareness of the P2P network's security state. We are proud to contribute to advancing blockchain safety and enhancing the Ethereum blockchain's security. To learn more about Tikuna, visit our website [19].

Phase I roadmap

Meaningful findings

We introduce three main contributions:

  • Our proposed machine learning anomaly detection approach can detect several attacks at the Ethereum P2P layer using peer event trace data in a simulation environment using the protocol labs’ framework testground.

  • We demonstrate how to detect eclipse attacks in a mainnet node by extracting custom-generated connection log data from the Ethereum client Prysm and utilizing an LSTM neural network.

  • We developed a custom exploit implementing a real-world eclipse attack. It was tested against a modified Prysm client in mainnet, so it did not forward spam to the network. With this, we could test the effectiveness of our approach.

Our team's initial step was to conduct in-depth research and analysis to gain a thorough understanding of the various existing P2P security approaches, i.e., state of the art techniques. Based on this, we developed a comprehensive plan of action outlining our Tikuna approach and methodology.

Subsequently, we proceeded to implement an LSTM based approach for Ethereum blockchain network security monitoring and anomaly detection. Additionally, our team developed and implemented three dashboards aimed at enhancing P2P security. These dashboards are the Ethereum P2P Network Status dashboard, the Beacon Node Monitor, and the Eclipse Attacks Dashboard.

Finally, throughout the development process, we rigorously tested our approach to ensure that it was functional. We also conducted several experiments in two distinct network environments, i.e., the simulated testground and the Ethereum mainnet, to thoroughly evaluate the effectiveness as well as the performance of the Tikuna approach.

The following is a sample of the training data used for Tikuna in the Ethereum mainnet, consisting of discovery connection data (UDP) from honest and simulated attacking peers. The normal data was collected from several nodes during 3 days under regular operation, and the malicious data was gathered from a single victim Ethereum node, using our developed exploit. Each line has several input features, including timestamp, IP, and Port removed from the peer table, IP and Port added to the peer table, and bucket where the peer is added.

The model and simulations

We have used RNNs for our research. They are a type of model frequently utilized for processing sequential data such as time series. These models are specialized for processing a sequence of values that are a function of time. RNNs can scale to long sequences that would not be practical for networks without sequence-based specialization. Most recurrent networks can also process sequences of variable length.

One of these models is especially of interest to this research. The long short-term memory model (LSTM) uses a gating mechanism to ensure proper information propagation through many time steps. LSTM networks have a specific memory cell and can capture long-term dependencies in sequential data. LSTM are valuable tools for language modeling problems. LSTM networks are a version of recurrent neural networks useful for long interrelated sequences of data. LSTM was chosen in this research for anomaly detection to find malicious discovery connections to an Ethereum client.

Following there is an example of the ML model output for mainnet where we obtained the best results detecting eclipse attacks, with 1’000,000 regular connection log lines for training and around 3,000 lines of eclipse attack logs for evaluation:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

We used standard measures metrics for intrusion detection with ML, such as f1, recall, precision, and accuracy. Please review the Github repository and stay tuned for our research paper publication if you want more details.

Dashboards

As part of our research, our team considered it important to demonstrate the results we obtained as well as a possible implementation. To achieve this, we created three Grafana dashboards that are integrated with Prometheus. These dashboards capture monitoring metrics and display the behavior of the P2P network, neighborhood, and Prysm node in real-time.

  • Ethereum P2P Network Status: The information that is displayed on the Ethereum P2P Network Status dashboard comes from our Prysm node and is obtained by monitoring the port 8080. It gives data on the neighborhood area as well as how this node interacts with the other nodes in the blockchain. This comprises the number of connected peers, the current and head slot, the various types of P2P libraries that are linked to our node together with their ratings, as well as information on the participation rate and network liveness of the P2P network. In addition to this, it provides information concerning validators, such as their total numbers, the status of their accounts, and the average amount of ether in their balances.

Fig. 1 The Ethereum P2P network status dashboard

  • Beacon Node Monitor: This dashboard belongs to the infrastructure of our own Ethereum node, which displays information such as the use of CPU, RAM, disk space, bandwidth within the network, and details about storage space. This information was retrieved from the Kubernetes cluster that serves as the host for the consensus node, which is the infrastructure in our particular instance.

Fig. 2 The beacon node monitor dashboard

  • Eclipse Attacks Dashboard: The creation of the final dashboard required the development of an interface that could record the potential eclipse attacks discovered by our machine learning algorithm and then input those records into the AlertManager component of Prometheus. After that, Grafana creates a table that displays a row for each identified eclipse attack in a given node.

Fig. 3 The eclipse attacks dashboard

At Tikuna, we believe that making a more secure and reliable P2P network requires accessibility and transparency, which is why we have developed a comprehensive User section for our proof-of-concept. Our User section offers detailed instructions and tutorials that guide users of all technical levels through the installation process and explain how to interpret the information presented in the dashboards. With Tikuna, a wider audience can benefit from our P2P monitoring system. We are proud to contribute to advancing blockchain security through our innovative ideas and commitment to user-friendly solutions. To learn more about Tikuna and how to use our monitoring system, visit our website [19] and browse through our User section.

The research paper

We are currently working on a draft research paper that is based on the findings and results obtained during this research grant. Once we receive feedback from EF, we intend to submit the research paper to an International Conference. The research paper is organized into five main chapters. The first chapter is an introduction to the Ethereum blockchain, which emphasizes the importance of understanding the risks associated with P2P blockchain networks and developing security-focused solutions to ensure their reliability. It also highlights the three main contributions of the paper, which include a proposed machine learning approach for detecting attacks in the P2P layer, a method for detecting eclipse attacks using custom-generated connection log data and LSTM neural networks, and a custom exploit for eclipse attack testing against a modified Prysm client in mainnet. The second chapter, titled "Related Work", provides an overview of the most recent works that address the security challenges of the Ethereum blockchain P2P networks. The third chapter introduces the Tikuna approach by describing each of the three steps and discussing the various types of blockchain P2P network attacks. The fourth chapter evaluates the effectiveness of the Tikuna approach using a simulation and mainnet connection dataset. The last chapter presents a summary of the proposed work, conclusions, and potential future research directions. Finally, the authors acknowledge that the work presented was supported by the Ethereum Foundation Academic Research Grants.

Next steps

We take immense pride in our team's accomplishments, and we extend our gratitude to everyone who played a part in ensuring the project's success. Our team's expertise, dedication, and collaborative ethos were crucial to delivering the first phase of the Tikuna project. This is just the beginning of our project as we aim to further develop Tikuna as a tool to contribute to the Ethereum community and other Blockchains. Our efforts will be focused on identifying additional attacks, minimizing false positives, detecting real-world incidents, and incorporating different Blockchain (including Ethereum) node clients. Moreover, we intend to investigate other areas of research where Tikuna can be utilized, such as Maximal Extractable Value (MEV).

We have applied for the second round of the EF academic research grants. Please see our application here. If you are interested in learning more about Tikuna or working with us, we welcome the opportunity to collaborate with you. Kindly reach out to us, and we'll be happy to hear from you.

Here you can find the Tikuna's full source code.



- - +
+ + \ No newline at end of file diff --git a/docs/about/sakundi/index.html b/docs/about/sakundi/index.html index 4cc2921..e13ab44 100644 --- a/docs/about/sakundi/index.html +++ b/docs/about/sakundi/index.html @@ -3,23 +3,19 @@ -About Sakundi | Blockchain Security Monitoring - - - - - - - +About Sakundi | Blockchain Security Monitoring + + +
-

About Sakundi


Sakundi is a blockchain security research organization that leverages AI and automation. Currently, we are developing our first solution for monitoring, detecting, and visualizing security incidents in Blockchain networks, decentralized applications (D-apps), and infrastructure. In addition, Sakundi's mission is to ensure the security and privacy of blockchains by developing easily deployable solutions based on scientific research. Our team is made up of specialists from all around the world that are expert in the fields of cybersecurity, blockchain, Fintech, and DeFi ecosystems.

+ + \ No newline at end of file diff --git a/docs/about/team/index.html b/docs/about/team/index.html index ad003cc..be890cf 100644 --- a/docs/about/team/index.html +++ b/docs/about/team/index.html @@ -3,23 +3,19 @@ -Our Team | Blockchain Security Monitoring - - - - - - - +Our Team | Blockchain Security Monitoring + + +
-

Our Team


Meet the group of professionals behind Sakundi.

Andrés Gómez Ramírez

Andrés Gómez Ramírez

Leader Security Researcher

Project leader of Tikuna. Ph.D. in cybersecurity from the University of Frankfurt and CERN. A blockchain security researcher at Edenia and Founder at Sakundi.

Francis Gómez Ramírez

Francis Gómez Ramírez

Senior DevOps Engineer

Computer scientist and specialist in project management with wide experience in banking industry and fintech. Co-founder at Sakundi

Loui Al Sardy

Loui Al Sardy

Security Researcher

Ph.D. candidate at Chair of Software Engineering at the University of Erlangen-Nuremberg. Co-founder at Sakundi

+ + \ No newline at end of file diff --git a/docs/monitoring/beacon-node/index.html b/docs/monitoring/beacon-node/index.html index d184d3c..20e7fa8 100644 --- a/docs/monitoring/beacon-node/index.html +++ b/docs/monitoring/beacon-node/index.html @@ -3,23 +3,19 @@ -Beacon Node Monitor | Blockchain Security Monitoring - - - - - - - +Beacon Node Monitor | Blockchain Security Monitoring + + + + + \ No newline at end of file diff --git a/docs/monitoring/eclipse-attacks/index.html b/docs/monitoring/eclipse-attacks/index.html index 8f995b5..9c2dabc 100644 --- a/docs/monitoring/eclipse-attacks/index.html +++ b/docs/monitoring/eclipse-attacks/index.html @@ -3,23 +3,19 @@ -Eclipse Attacks Dashboard | Blockchain Security Monitoring - - - - - - - +Eclipse Attacks Dashboard | Blockchain Security Monitoring + + + + + \ No newline at end of file diff --git a/docs/monitoring/network-eth2/index.html b/docs/monitoring/network-eth2/index.html index c284d8f..e83943a 100644 --- a/docs/monitoring/network-eth2/index.html +++ b/docs/monitoring/network-eth2/index.html @@ -3,23 +3,19 @@ -Ethereum P2P Network Status | Blockchain Security Monitoring - - - - - - - +Ethereum P2P Network Status | Blockchain Security Monitoring + + +
-

Ethereum P2P Network Status


We have created a monitoring dashboard with our available metrics for the ETH 2 peer-to-peer network:

+ + \ No newline at end of file diff --git a/docs/research/glossary/index.html b/docs/research/glossary/index.html index 9106b2c..089d5fe 100644 --- a/docs/research/glossary/index.html +++ b/docs/research/glossary/index.html @@ -3,23 +3,19 @@ -Glossary | Blockchain Security Monitoring - - - - - - - +Glossary | Blockchain Security Monitoring + + +
-

Glossary

List of terms recommended to understand the contents of this research grant.

A

Account

An object that has an address, a balance, a nonce, and possibly some storage and code as well. An account may be either a contract account or an externally owned account (EOA).


Address

This is typically an EOA or contract that can receive (destination address) or send (source address) blockchain transactions. Furthermore, it is the rightmost 160 bits of an ECDSA public key's Keccak hash.


API (Application Programming Interface)

Set of functions and procedures that allow the creation of applications that access the characteristics or data of an operating system, application or other specific service.


B

Backend

Backend is the data access layer of a software, which is not directly accessible by users, it contains the logic of the application that handles said data. The Backend accesses the server, which is a specialized application that understands the way the browser requests information.


Base Fee

The reserve price for each block is known as the "base fee." It is the minimum gas fee a user must pay for a transaction to be included in the subsequent block.


Blacklist

A blacklist is a measure that a blockchain node can take to prevent it from blocking any transaction that has been authorized by a defined list of blocked accounts or any transaction that interacts with a defined list of contracts. Each node can define its own blacklist, which cannot be seen publicly.


Block

Blocks are files where the data belonging to the blockchain is permanently recorded. They store some or all of the most recent transactions that have not yet entered any previous blocks. A block is a permanent store of records that once written cannot be altered or deleted.


Block Explorer

A block explorer is a program or website accessible through a browser that allows a user to search and navigate through the blocks of a blockchain, their contents and their relevant details.


Block Height

It is defined as the number of blocks in the chain between a given block and the first block in the block chain. For example a block height of 500 refers to block number 500 of that chain.


Block Header

The block header is a collection of block-specific metadata and a summary of the transactions contained in the execution payload.


Block Rewards

Bitcoin block reward refers to the new bitcoins that are awarded by the blockchain network to eligible cryptocurrency miners for each block they mine successfully.


Blockchain

A growing list of records, called blocks, that are linked by cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (usually represented as a Merkle tree).


Block Validation

Verifying that a new block has valid transactions and signatures, builds on the heaviest historical chain, and adheres to all other consensus requirements. Valid blocks are added to the end of the chain and distributed to the network, where invalid blocks are discarded.


Bytecode Canonical

It is used for communication between different data formats. A form of business application integration, it aims to reduce costs and standardize the agreed definitions of data associated with the integration of business systems.


C

Chain ID

A chain ID is a blockchain configuration that affects the formation of an address. The binary transaction link between the address and the chain ID makes it impossible to move transactions between different blockchain networks.


Contract Account

An account that contains code that is executed whenever it receives a transaction from another account (EOA or contract).


D

DApps

They are digital applications or programs run on a blockchain or P2P computer network instead of a single computer, these are outside the reach and control of a single authority.


DevOps

A set of practices that combines software development and IT operations. Its goal is to shorten the life cycle of systems development and provide continuous delivery with high-quality software.


Discovery

The method by which an Ethereum node identifies other nodes to connect to.


Distributed Hash Table (DHT)

A data structure comprising (key, value) pairs that Ethereum nodes use to identify peers to connect with and choose which protocols to utilize for communication.


Docker

A set of platform as a service products that uses virtualization at the operating system level to deliver software in packages called containers. Containers are isolated from each other and bundle their own software, libraries, and configuration files; they can communicate with each other through well-defined channels. All containers are run by a single core operating system, therefore using fewer resources than virtual machines.


E

Ether

The native cryptocurrency that is used by the Ethereum ecosystem and that is used to pay for gas fees when transactions are being executed.


F

Fork

A change in protocol causing the creation of an alternative chain, or a temporal divergence in two potential block paths during mining.


G

Genesis JSON

genesis.json is the file that defines the Genesis block, which is the starting block of the blockchain. This is like the "configuration" of your blockchain. For example, the chain configuration and the level of difficulty to extract blocks.


Gas

A virtual fuel used in Ethereum to execute smart contracts. The EVM uses an accounting mechanism to measure the consumption of gas and limit the consumption of computing resources.


Gas Limit

The maximum amount of gas a transaction or block may consume.


GitHub

A platform that provides hosting for version control of software development, in addition, it allows to manage distributed version control and the functionality of source code management, as well as its own characteristics. It provides access control and various collaboration features such as bug tracking, feature requests, task management, and wikis for each project.


H

Hash

Is the process of transforming a string into a fixed-length key or value. A data string will always produce the same hash, so it is used as a means of checking the content of the string against the expected content.


Head Block

The Head Block is the last block written on the blockchain, stored in reversible blocks.


I

IAC (Infrastructure as Code)

Is the process of managing computer data centers through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools.


IBC (Inter-Blockchain Communication)

A tool by which multiple blockchains can communicate with each other. This can be used to send tokens from one chain to another, or to expand the capacity of a network by increasing the number of chains running in parallel.


Immutable

Term used to refer to something that cannot be changed.


IPFS

It is a distributed storage mechanism for putting files online for decentralized storage.


Issue

Issues are a great way to track tasks, improvements and bugs for your projects through the GitHub platform.


J

K

Kubernetes

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.


L

Latency

Period of time that elapses between the communication of a packet through the network.


Launch

The term used to signify the beginning of a blockchain.


M

Merkle Tree

A Merkle Tree is a tree in which every leaf node is labelled with the hash of a data block, and every non-leaf node is labelled with the cryptographic hash of the labels of its child nodes. Hash trees allow efficient and secure verification of the contents of large data structures.


Message

An internal transaction that is never serialized and only sent within the EVM.


Multi-Sig

Multisig is a short term for multiple signatures.It’s used to describe the case in which one requires more than one account's permission to execute a transaction.


N

Network

The Ethereum network is a peer-to-peer network that broadcasts transactions and blocks to every Ethereum node (network participant).


Nodes

node is the fundamental basis of blockchain technology, they make up the infrastructure of a blockchain. All nodes in a blockchain are connected to each other and constantly exchange the latest data in the blockchain so that all nodes are kept up to date.


O

Oracle

An oracle is a system that makes off-chain data available on-chain. An example of this would be an oracle that takes the score of a basketball game and puts it on-chain for smart contracts to consume and base decisions on.


P

Peer

Computers running Ethereum client software that have identical copies of the blockchain and are connected to one another.


Private Key (aka Secret Key)

A private key is a secret key used to sign transactions. In blockchain networks, a private key's authority is determined by it's mapping to a public key on the network.


Proof of Work

Proof of Work is a consensus mechanism used by many blockchains, most notably by Bitcoin. The miner of the next block is the first to solve a hashing problem. The difficulty of this problem increases as more hashing power attempts to solve this problem at the same time. The ever-increasing amount of work that has gone into a blockchain over time is what protects the chain's history.


Public Key

A publicly available key that can be authorized to permissions of an account and can be used to identify the origin transaction. A public key can be inferred from a signature.


Pull Request

Pull requests let you tell others about changes you've pushed to a branch in a repository on GitHub.

Once a pull request is opened, you can discuss and review the potential changes with collaborators and add follow-up commits before your changes are merged into the base branch.


Q

R

Receipt

Data returned by an Ethereum client to represent the result of a particular transaction, including a hash of the transaction, its block number, the amount of gas used, and, in case of deployment of a smart contract, the address of the contract.


Recursive Length Prefix (RLP)

Recursive Length Prefix. An encoding standard designed by the Ethereum developers to encode and serialize objects (data structures) of arbitrary complexity and length.


S

Serialization

The process of converting a data structure into a sequence of bytes.


Smart Contract

A smart contract is a computer protocol intended to facilitate, verify, or enforce the negotiation or performance of a contract.


Solidity

A procedural programming language having comparable syntax to JavaScript, C++, and Java. The most commonly used and popular language for Ethereum smart contracts.


Stake

Tokens deposited in a smart contract is referred to as your stake.


State

A snapshot of all balances and data at a particular point in time on the blockchain, typically referring to the condition at a specific block.


Syncing

The process of downloading the entire latest version of a blockchain to a node.


T

TestNet

A test network or testnet is an instantiation of the EOSIO platform that is intended for testing purposes. Generally, the native token has no value and is given away to developers so they can test.


Tokens

The unit of measurement of a cryptocurrency.


TPS

Transactions Per Second. A metric by which the performance of a blockchain is measured.


Transactions

It is a combination of one or more actions. Usually the result of executing a smart contract.


U

Unhosted Wallet

A type of self-custody wallet that lets users keep their cryptocurrency balances independent of exchanges or third parties.


Utility Coin

A token typically providing holders with the ability to access, via an encrypted key, a particular blockchain or network for purposes of accessing certain benefits or functionality on that blockchain or platform. Utility tokens are typically not intended for use as a currency or means of payment.


V

Validator

A node is responsible for storing data, executing transactions, and adding new blocks to the blockchain in a proof-of-stake system. For the validator software to be activated, 32 ETH must be staked.


W

Wallet

Wallets are clients that store keys that may or may not be associated with the permissions of one or more accounts. Ideally a wallet has a locked (encrypted) and unlocked (decrypted) state that is protected by a high entropy password.


Whisper

A decentralized (P2P) messaging service. It is used along with Web3 and Swarm to build DApps.


Whitelist

A whitelist is a subjective measure that a blockchain node can implement to allow transactions to be placed in a block only if they are listed in your account or contract whitelist. Each node can specify its own whitelist, which cannot be seen publicly.


White Paper

A document released by developers that explain the technology and purpose of the project they are working on. The document tells prospective investors how the cryptocurrency was conceived and highlights its purpose.


X

Y

Z

Zero Address

A special Ethereum address, composed entirely of zeros, that is specified as the destination address of a contract creation transaction.


+ + \ No newline at end of file diff --git a/docs/research/intro/index.html b/docs/research/intro/index.html index 6791a0c..38f623c 100644 --- a/docs/research/intro/index.html +++ b/docs/research/intro/index.html @@ -3,23 +3,19 @@ -Welcome to Tikuna | Blockchain Security Monitoring - - - - - - - +Welcome to Tikuna | Blockchain Security Monitoring + + +
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Welcome to Tikuna


Tikuna, a proof-of-concept P2P network security monitoring system for the Ethereum blockchain. By leveraging Machine Learning techniques, it will extract security and performance insights for early detection of relevant incidents. Tikuna will bring improved visibility to the security state of the P2P network.

About the Project

We will investigate how to apply Machine Learning algorithms to monitor the security of the Ethereum P2P network for early detection of a few specific attacks. We will develop Tikuna, a proof-of-concept tool (open source so that every user can use it) derived from such research, and will provide a scientific report based on the findings of applying the tool to the mainnet network. We will also provide a simple dashboard for the community to observe the insights that Tikuna provides. Additionally, we will create a draft of a scientific paper based on the research results.

Project Purpose

Tikuna will help the Ethereum community to gain visibility on the status of the P2P network and be aware of potential attacks.

The overall goal of the project is to support the Ethereum community by providing them with an intelligent state of the art tool capable of collecting security-related information from the state of the P2P network and improve the network visibility by providing insights about its current state. Such measurable information and insights criteria can help us to measure the success of our approach by the number of identified incidents and the number of discovered insights. Therefore, our tool will play a major role in improving the overall Ethereum network security.

Project Roadmap

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Project References


- - +
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State of the Art


Ethereum was formally introduced by Vitalik Buterin in his whitepaper in 2014 [2] and launched in 2015; it is considered one of the most well-known open-source public cryptocurrency blockchain platforms that support smart contract functionality with Ether (ETH or Ξ) as it is native cryptocurrency and Solidity its programming language. According to [1], there were a total number of 105.58 million transactions and 1.45 million smart contracts created in the first quarter (Q1) of 2022. In addition, concerning market capitalization, Ethereum is the second largest cryptocurrency after Bitcoin, with around $234 billion as of August 2022 [3].

Furthermore, Ethereum allows developers to create decentralized applications (dApps) in various sectors such as decentralized finance (DeFi) [4], gaming [5,6], Non-fungible tokens (NFTs) [7], and Internet-of-Things (IoT) [10] that run on a peer-to-peer (P2P) network. Therefore, minimizing the chances of fraud, censorship, downtime, or third-party involvement and making them more reliable. Additionally, the Ethereum public mainnet has just gone through a significant upgrade to Ethereum 2 (officially after renaming called The Merge ($ETH) [8, 9]) to improve its performance, efficiency, and scalability.

Blockchains have many layers, such as the consensus layer [11-13], scripting layer, network layer, or DApps, but what we are interested in is the lowest level layer in the blockchain, namely the P2P network that allows the nodes to interconnect to each other and share information, which is a vital component of the Ethereum blockchain. The basic idea behind the P2P network is that a group of computers called nodes are connected to share data without the need for a centralized computer, i.e., all the nodes on the network have the same capabilities without having any special privileges, such as the one used in the file sharing systems Napster, LimeWire, and Gnutella.

The peer-to-peer (P2P) networking is the fundamental component that serves as the foundation for any blockchain technology. But, similar to other forms of technology, P2P networks may be affected by a number of security issues. Moreover, adversaries can exploit some of the blockchain P2P network vulnerabilities to carry out several attack vectors on the blockchain, such as eclipse attack, selfish mining attacks, Sybil attacks, or DDoS attacks. Therefore, to address both the different attack vectors on the Ethereum platform and the P2P network security vulnerabilities, many researchers have recently started focusing their research in this direction.

In the following are some of the most recent works that address the security challenges of the Ethereum blockchain P2P networks:

  • This paper [14] generally focuses on the security issues of each layer in the Ethereum blockchain such as the network layer by providing an in-depth analysis covering the following three areas:

    1. Its potential attacks like eclipse attack and account hijacking attack.
    2. The vulnerabilities that lead to them for example the unlimited nodes creation vulnerability, the uncapped incoming connections vulnerability.
    3. The effects of each incident such as double spending, or denial of Service (DoS).

    In addition to providing an overview of the effectiveness and the limitations of the existing Intrusion Detection Systems (IDS) as a defense technique against these attacks.

  • This paper [15] proposes the GossipSub protocol and its two components, the mesh construction and the score function. The GossipSub is a messaging protocol that enables rapid and robust message transmission in permissionless networks, such as the Ethereum 2.0 network layer, to make it, on the one hand, more secure against various types of attacks (such as the Sybil attack, the Eclipse attack, and the Cold Boot attack), and on the other hand, to facilitate a faster transmission of the messages within the network. In addition, the authors describe some of the countermeasures featured in the GossipSub protocol, such as controlled mesh maintenance, opportunistic grafting, and adaptive gossip distribution. In conclusion, the writers evaluate the GossipSub protocol against various attacks and demonstrate its resistance to these attacks.

  • In this study [16], the authors first highlight some of the Eth2 network structure, the network ecosystem, and the potential dangers it could pose. In addition, the writers comprehensively investigate the Eth2 mainnet's P2P network by developing a monitoring tool called Armiarma. This tool consists of two components, the Armiarma crawler and the Armiarma analyzer, to address the absence of information and the missing insights regarding the performance of the GossipSub protocol and its peers on the Eth2 main network. Furthermore, an experiment was conducted to evaluate the capabilities of the proposed approach, and an analysis of the gathered outcomes was grouped into the following three parts:

    1. Eth2 Network Analysis
    2. Eth2 Clients Interaction
    3. Individual Peer Analysis

    Moreover, the analysis identifies several issues with the Eth2 Beacon Chain, including geographical, implementation, and physical hosting of the Eth2 validators. Finally, the authors conclude that the network is behaving healthily; however, some decentralization-related issues still need to be considered.

  • This research [17] discusses "the eclipse attacks" on the Ethereum P2P Network. An eclipse attack is an attack that allows an adversary to isolate a target node within the P2P network by gaining complete control of a node's access to information or control over everything that the node sees. This attack is discussed in the context of the Ethereum P2P Network. The authors developed an eclipse-attack detection model called ETH-EDS that mainly targets the Ethereum platform. This model used the random forest classification technique to examine both normal and attack data packets in the network. The collected data packets included details like the size of the tags packets, the frequency with which they were accessed, and the access time. The findings of the experiments show that malicious network nodes could be identified with a high degree of precision.

+ + \ No newline at end of file diff --git a/docs/user-section/how-to-use-tikuna/index.html b/docs/user-section/how-to-use-tikuna/index.html index 62bcb20..94799fd 100644 --- a/docs/user-section/how-to-use-tikuna/index.html +++ b/docs/user-section/how-to-use-tikuna/index.html @@ -3,23 +3,19 @@ -How to Use Tikuna | Blockchain Security Monitoring - - - - - - - +How to Use Tikuna | Blockchain Security Monitoring + + +
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How to Use Tikuna


Before starting

  • Install docker https://docs.docker.com/engine/install/. If you have previously installed Docker, verify it using the docker --version command and proceed to the next step.
  • Create a local directory named /data; if this directory already exists, proceed to the next step.
  • Copy the folders that contain the data to the /data directory created in the previous step.

Installation

To run this project, install it locally using:

- $ git clone https://github.com/sakundi/tikuna
- $ cd tikuna
- $ docker build -t tikuna:latest .

Quick start

To begin using Tikuna, perform the steps below.

  • Inside the Tikuna project directory, go to the bin folder and execute the following command to start a docker container:

       $ ./start_tikuna.sh
  • Once inside the docker container execute the following command to launch a Jupyter Notebook session in which the machine learning algorithms can be executed using Ethereum data:

       $ ./bin/start_jupyter_notebook.sh
  • After executing the previous command, you only need to open a browser, and then copy & paste the displayed URL.

  • In the web browser, navigate to the analysis folder, and then click over it.
  • Click on ethereum_lstm_log.ipynb file.
  • For each step, click the Run button and wait until the results are displayed on the screen.
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Understanding the data behind the Dashboards


P2P Network status on ETH2:

This dashboard shows all the important information contained historically inside the node. There is disposed of in two status: Neighborhood & Network.

  • The neighborhood status refers to how the own node is interacting with other nodes in the blockchain. It can be found important stats such as:

Peer Count:

How many other nodes in the beacon network the node itself is interacting with.

Max inactivity:

Is a score that reflects the current inactivity of the validators.

Libp2p Peers pie chart:

Shows how the agents are distributed among neighboring peers, which type of Libp2p client are using and what’s the most used.

Libp2p Peers average scores:

The average score obtain in terms of the metrics and performance.

  • In the other hand, the network status shows a summarized data, measured by the own node, about the general aspects in the network status such as:

Participation rate:

How many ETH Balances are attesting (validating) to the network.

Network Liveness:

This metric expresses the distance between the current finalized epoch and the expected finalized epoch based on the genesis time.

Epochs:

A period of time during which a specific set of chosen validators are responsible for creating new blocks and adding them to the blockchain.

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Página No Encontrada

No pudimos encontrar lo que buscaba.

Comuníquese con el dueño del sitio que lo vinculó a la URL original y hágale saber que su vínculo está roto.

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)}.socialMediaLinks{color:#fff;flex-wrap:wrap;justify-content:space-around}.skipToContent_fXgn{background-color:var(--ifm-background-surface-color);color:var(--ifm-color-emphasis-900);left:100%;padding:calc(var(--ifm-global-spacing)/2) 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)}.socialMediaLinks{color:#fff;flex-wrap:wrap;justify-content:space-around}.DocSearch-Button,.DocSearch-Button-Container{align-items:center;display:flex}.DocSearch-Button{background:var(--docsearch-searchbox-background);border:0;border-radius:40px;color:var(--docsearch-muted-color);cursor:pointer;font-weight:500;height:36px;justify-content:space-between;padding:0 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facets attribute of the helper configuration");return this.setQueryParameters({facetsRefinements:f.toggleRefinement(this.facetsRefinements,e,t)})},toggleExcludeFacetRefinement:function(e,t){if(!this.isConjunctiveFacet(e))throw new Error(e+" is not defined in the facets attribute of the helper configuration");return this.setQueryParameters({facetsExcludes:f.toggleRefinement(this.facetsExcludes,e,t)})},toggleDisjunctiveFacetRefinement:function(e,t){if(!this.isDisjunctiveFacet(e))throw new Error(e+" is not defined in the disjunctiveFacets attribute of the helper configuration");return this.setQueryParameters({disjunctiveFacetsRefinements:f.toggleRefinement(this.disjunctiveFacetsRefinements,e,t)})},toggleHierarchicalFacetRefinement:function(e,t){if(!this.isHierarchicalFacet(e))throw new Error(e+" is not defined in the hierarchicalFacets attribute of the helper configuration");var r=this._getHierarchicalFacetSeparator(this.getHierarchicalFacetByName(e)),i={};return void 0!==this.hierarchicalFacetsRefinements[e]&&this.hierarchicalFacetsRefinements[e].length>0&&(this.hierarchicalFacetsRefinements[e][0]===t||0===this.hierarchicalFacetsRefinements[e][0].indexOf(t+r))?-1===t.indexOf(r)?i[e]=[]:i[e]=[t.slice(0,t.lastIndexOf(r))]:i[e]=[t],this.setQueryParameters({hierarchicalFacetsRefinements:n({},i,this.hierarchicalFacetsRefinements)})},addHierarchicalFacetRefinement:function(e,t){if(this.isHierarchicalFacetRefined(e))throw new Error(e+" is already refined.");if(!this.isHierarchicalFacet(e))throw new Error(e+" is not defined in the hierarchicalFacets attribute of the helper configuration.");var r={};return r[e]=[t],this.setQueryParameters({hierarchicalFacetsRefinements:n({},r,this.hierarchicalFacetsRefinements)})},removeHierarchicalFacetRefinement:function(e){if(!this.isHierarchicalFacetRefined(e))return this;var t={};return t[e]=[],this.setQueryParameters({hierarchicalFacetsRefinements:n({},t,this.hierarchicalFacetsRefinements)})},toggleTagRefinement:function(e){return this.isTagRefined(e)?this.removeTagRefinement(e):this.addTagRefinement(e)},isDisjunctiveFacet:function(e){return this.disjunctiveFacets.indexOf(e)>-1},isHierarchicalFacet:function(e){return void 0!==this.getHierarchicalFacetByName(e)},isConjunctiveFacet:function(e){return this.facets.indexOf(e)>-1},isFacetRefined:function(e,t){return!!this.isConjunctiveFacet(e)&&f.isRefined(this.facetsRefinements,e,t)},isExcludeRefined:function(e,t){return!!this.isConjunctiveFacet(e)&&f.isRefined(this.facetsExcludes,e,t)},isDisjunctiveFacetRefined:function(e,t){return!!this.isDisjunctiveFacet(e)&&f.isRefined(this.disjunctiveFacetsRefinements,e,t)},isHierarchicalFacetRefined:function(e,t){if(!this.isHierarchicalFacet(e))return!1;var r=this.getHierarchicalRefinement(e);return t?-1!==r.indexOf(t):r.length>0},isNumericRefined:function(e,t,r){if(void 0===r&&void 0===t)return Boolean(this.numericRefinements[e]);var n=this.numericRefinements[e]&&void 0!==this.numericRefinements[e][t];if(void 0===r||!n)return n;var a,s,c=o(r),u=void 0!==(a=this.numericRefinements[e][t],s=c,i(a,(function(e){return l(e,s)})));return n&&u},isTagRefined:function(e){return-1!==this.tagRefinements.indexOf(e)},getRefinedDisjunctiveFacets:function(){var e=this,t=a(Object.keys(this.numericRefinements).filter((function(t){return Object.keys(e.numericRefinements[t]).length>0})),this.disjunctiveFacets);return Object.keys(this.disjunctiveFacetsRefinements).filter((function(t){return e.disjunctiveFacetsRefinements[t].length>0})).concat(t).concat(this.getRefinedHierarchicalFacets()).sort()},getRefinedHierarchicalFacets:function(){var e=this;return a(this.hierarchicalFacets.map((function(e){return e.name})),Object.keys(this.hierarchicalFacetsRefinements).filter((function(t){return e.hierarchicalFacetsRefinements[t].length>0}))).sort()},getUnrefinedDisjunctiveFacets:function(){var e=this.getRefinedDisjunctiveFacets();return this.disjunctiveFacets.filter((function(t){return-1===e.indexOf(t)}))},managedParameters:["index","facets","disjunctiveFacets","facetsRefinements","hierarchicalFacets","facetsExcludes","disjunctiveFacetsRefinements","numericRefinements","tagRefinements","hierarchicalFacetsRefinements"],getQueryParams:function(){var e=this.managedParameters,t={},r=this;return Object.keys(this).forEach((function(n){var i=r[n];-1===e.indexOf(n)&&void 0!==i&&(t[n]=i)})),t},setQueryParameter:function(e,t){if(this[e]===t)return this;var r={};return r[e]=t,this.setQueryParameters(r)},setQueryParameters:function(e){if(!e)return this;var t=m.validate(this,e);if(t)throw t;var r=this,n=m._parseNumbers(e),i=Object.keys(this).reduce((function(e,t){return e[t]=r[t],e}),{}),a=Object.keys(n).reduce((function(e,t){var r=void 0!==e[t],i=void 0!==n[t];return r&&!i?u(e,[t]):(i&&(e[t]=n[t]),e)}),i);return new this.constructor(a)},resetPage:function(){return void 0===this.page?this:this.setPage(0)},_getHierarchicalFacetSortBy:function(e){return e.sortBy||["isRefined:desc","name:asc"]},_getHierarchicalFacetSeparator:function(e){return e.separator||" > "},_getHierarchicalRootPath:function(e){return e.rootPath||null},_getHierarchicalShowParentLevel:function(e){return"boolean"!=typeof e.showParentLevel||e.showParentLevel},getHierarchicalFacetByName:function(e){return i(this.hierarchicalFacets,(function(t){return t.name===e}))},getHierarchicalFacetBreadcrumb:function(e){if(!this.isHierarchicalFacet(e))return[];var t=this.getHierarchicalRefinement(e)[0];if(!t)return[];var r=this._getHierarchicalFacetSeparator(this.getHierarchicalFacetByName(e));return t.split(r).map((function(e){return e.trim()}))},toString:function(){return JSON.stringify(this,null,2)}},e.exports=m},6673:(e,t,r)=>{"use strict";e.exports=function(e){return function(t,r){var n=e.hierarchicalFacets[r],o=e.hierarchicalFacetsRefinements[n.name]&&e.hierarchicalFacetsRefinements[n.name][0]||"",h=e._getHierarchicalFacetSeparator(n),f=e._getHierarchicalRootPath(n),l=e._getHierarchicalShowParentLevel(n),m=a(e._getHierarchicalFacetSortBy(n)),d=t.every((function(e){return e.exhaustive})),p=function(e,t,r,n,a){return function(o,h,f){var l=o;if(f>0){var m=0;for(l=o;m{"use strict";var n=r(8965),i=r(9110),a=r(2909),s=r(849),c=r(3917),u=r(7577),o=r(4728),h=r(8601),f=a.escapeFacetValue,l=a.unescapeFacetValue,m=r(6673);function d(e){var t={};return e.forEach((function(e,r){t[e]=r})),t}function p(e,t,r){t&&t[r]&&(e.stats=t[r])}function v(e,t,r){var a=t[0];this._rawResults=t;var u=this;Object.keys(a).forEach((function(e){u[e]=a[e]}));var h=o({persistHierarchicalRootCount:!1},r);Object.keys(h).forEach((function(e){u[e]=h[e]})),this.processingTimeMS=t.reduce((function(e,t){return void 0===t.processingTimeMS?e:e+t.processingTimeMS}),0),this.disjunctiveFacets=[],this.hierarchicalFacets=e.hierarchicalFacets.map((function(){return[]})),this.facets=[];var f=e.getRefinedDisjunctiveFacets(),v=d(e.facets),g=d(e.disjunctiveFacets),y=1,R=a.facets||{};Object.keys(R).forEach((function(t){var r,n,i=R[t],o=(r=e.hierarchicalFacets,n=t,s(r,(function(e){return(e.attributes||[]).indexOf(n)>-1})));if(o){var h=o.attributes.indexOf(t),f=c(e.hierarchicalFacets,(function(e){return e.name===o.name}));u.hierarchicalFacets[f][h]={attribute:t,data:i,exhaustive:a.exhaustiveFacetsCount}}else{var l,m=-1!==e.disjunctiveFacets.indexOf(t),d=-1!==e.facets.indexOf(t);m&&(l=g[t],u.disjunctiveFacets[l]={name:t,data:i,exhaustive:a.exhaustiveFacetsCount},p(u.disjunctiveFacets[l],a.facets_stats,t)),d&&(l=v[t],u.facets[l]={name:t,data:i,exhaustive:a.exhaustiveFacetsCount},p(u.facets[l],a.facets_stats,t))}})),this.hierarchicalFacets=n(this.hierarchicalFacets),f.forEach((function(r){var n=t[y],s=n&&n.facets?n.facets:{},h=e.getHierarchicalFacetByName(r);Object.keys(s).forEach((function(t){var r,f=s[t];if(h){r=c(e.hierarchicalFacets,(function(e){return e.name===h.name}));var m=c(u.hierarchicalFacets[r],(function(e){return e.attribute===t}));if(-1===m)return;u.hierarchicalFacets[r][m].data=o({},u.hierarchicalFacets[r][m].data,f)}else{r=g[t];var d=a.facets&&a.facets[t]||{};u.disjunctiveFacets[r]={name:t,data:i({},f,d),exhaustive:n.exhaustiveFacetsCount},p(u.disjunctiveFacets[r],n.facets_stats,t),e.disjunctiveFacetsRefinements[t]&&e.disjunctiveFacetsRefinements[t].forEach((function(n){!u.disjunctiveFacets[r].data[n]&&e.disjunctiveFacetsRefinements[t].indexOf(l(n))>-1&&(u.disjunctiveFacets[r].data[n]=0)}))}})),y++})),e.getRefinedHierarchicalFacets().forEach((function(r){var n=e.getHierarchicalFacetByName(r),a=e._getHierarchicalFacetSeparator(n),s=e.getHierarchicalRefinement(r);0===s.length||s[0].split(a).length<2||t.slice(y).forEach((function(t){var r=t&&t.facets?t.facets:{};Object.keys(r).forEach((function(t){var o=r[t],h=c(e.hierarchicalFacets,(function(e){return e.name===n.name})),f=c(u.hierarchicalFacets[h],(function(e){return e.attribute===t}));if(-1!==f){var l={};if(s.length>0&&!u.persistHierarchicalRootCount){var m=s[0].split(a)[0];l[m]=u.hierarchicalFacets[h][f].data[m]}u.hierarchicalFacets[h][f].data=i(l,o,u.hierarchicalFacets[h][f].data)}})),y++}))})),Object.keys(e.facetsExcludes).forEach((function(t){var r=e.facetsExcludes[t],n=v[t];u.facets[n]={name:t,data:R[t],exhaustive:a.exhaustiveFacetsCount},r.forEach((function(e){u.facets[n]=u.facets[n]||{name:t},u.facets[n].data=u.facets[n].data||{},u.facets[n].data[e]=0}))})),this.hierarchicalFacets=this.hierarchicalFacets.map(m(e)),this.facets=n(this.facets),this.disjunctiveFacets=n(this.disjunctiveFacets),this._state=e}function g(e,t){function r(e){return e.name===t}if(e._state.isConjunctiveFacet(t)){var n=s(e.facets,r);return n?Object.keys(n.data).map((function(r){var i=f(r);return{name:r,escapedValue:i,count:n.data[r],isRefined:e._state.isFacetRefined(t,i),isExcluded:e._state.isExcludeRefined(t,r)}})):[]}if(e._state.isDisjunctiveFacet(t)){var i=s(e.disjunctiveFacets,r);return i?Object.keys(i.data).map((function(r){var n=f(r);return{name:r,escapedValue:n,count:i.data[r],isRefined:e._state.isDisjunctiveFacetRefined(t,n)}})):[]}if(e._state.isHierarchicalFacet(t)){var a=s(e.hierarchicalFacets,r);if(!a)return a;var c=e._state.getHierarchicalFacetByName(t),u=e._state._getHierarchicalFacetSeparator(c),o=l(e._state.getHierarchicalRefinement(t)[0]||"");0===o.indexOf(c.rootPath)&&(o=o.replace(c.rootPath+u,""));var h=o.split(u);return h.unshift(t),y(a,h,0),a}}function y(e,t,r){e.isRefined=e.name===(t[r]&&t[r].trim()),e.data&&e.data.forEach((function(e){y(e,t,r+1)}))}function R(e,t,r,n){if(n=n||0,Array.isArray(t))return e(t,r[n]);if(!t.data||0===t.data.length)return t;var a=t.data.map((function(t){return R(e,t,r,n+1)})),s=e(a,r[n]);return i({data:s},t)}function F(e,t){var r=s(e,(function(e){return e.name===t}));return r&&r.stats}function b(e,t,r,n,i){var a=s(i,(function(e){return e.name===r})),c=a&&a.data&&a.data[n]?a.data[n]:0,u=a&&a.exhaustive||!1;return{type:t,attributeName:r,name:n,count:c,exhaustive:u}}v.prototype.getFacetByName=function(e){function t(t){return t.name===e}return s(this.facets,t)||s(this.disjunctiveFacets,t)||s(this.hierarchicalFacets,t)},v.DEFAULT_SORT=["isRefined:desc","count:desc","name:asc"],v.prototype.getFacetValues=function(e,t){var r=g(this,e);if(r){var n,a=i({},t,{sortBy:v.DEFAULT_SORT,facetOrdering:!(t&&t.sortBy)}),s=this;if(Array.isArray(r))n=[e];else n=s._state.getHierarchicalFacetByName(r.name).attributes;return R((function(e,t){if(a.facetOrdering){var r=function(e,t){return e.renderingContent&&e.renderingContent.facetOrdering&&e.renderingContent.facetOrdering.values&&e.renderingContent.facetOrdering.values[t]}(s,t);if(r)return function(e,t){var r=[],n=[],i=(t.order||[]).reduce((function(e,t,r){return e[t]=r,e}),{});e.forEach((function(e){var t=e.path||e.name;void 0!==i[t]?r[i[t]]=e:n.push(e)})),r=r.filter((function(e){return e}));var a,s=t.sortRemainingBy;return"hidden"===s?r:(a="alpha"===s?[["path","name"],["asc","asc"]]:[["count"],["desc"]],r.concat(h(n,a[0],a[1])))}(e,r)}if(Array.isArray(a.sortBy)){var n=u(a.sortBy,v.DEFAULT_SORT);return h(e,n[0],n[1])}if("function"==typeof a.sortBy)return function(e,t){return t.sort(e)}(a.sortBy,e);throw new Error("options.sortBy is optional but if defined it must be either an array of string (predicates) or a sorting function")}),r,n)}},v.prototype.getFacetStats=function(e){return this._state.isConjunctiveFacet(e)?F(this.facets,e):this._state.isDisjunctiveFacet(e)?F(this.disjunctiveFacets,e):void 0},v.prototype.getRefinements=function(){var e=this._state,t=this,r=[];return Object.keys(e.facetsRefinements).forEach((function(n){e.facetsRefinements[n].forEach((function(i){r.push(b(e,"facet",n,i,t.facets))}))})),Object.keys(e.facetsExcludes).forEach((function(n){e.facetsExcludes[n].forEach((function(i){r.push(b(e,"exclude",n,i,t.facets))}))})),Object.keys(e.disjunctiveFacetsRefinements).forEach((function(n){e.disjunctiveFacetsRefinements[n].forEach((function(i){r.push(b(e,"disjunctive",n,i,t.disjunctiveFacets))}))})),Object.keys(e.hierarchicalFacetsRefinements).forEach((function(n){e.hierarchicalFacetsRefinements[n].forEach((function(i){r.push(function(e,t,r,n){var i=e.getHierarchicalFacetByName(t),a=e._getHierarchicalFacetSeparator(i),c=r.split(a),u=s(n,(function(e){return e.name===t})),o=c.reduce((function(e,t){var r=e&&s(e.data,(function(e){return e.name===t}));return void 0!==r?r:e}),u),h=o&&o.count||0,f=o&&o.exhaustive||!1,l=o&&o.path||"";return{type:"hierarchical",attributeName:t,name:l,count:h,exhaustive:f}}(e,n,i,t.hierarchicalFacets))}))})),Object.keys(e.numericRefinements).forEach((function(t){var n=e.numericRefinements[t];Object.keys(n).forEach((function(e){n[e].forEach((function(n){r.push({type:"numeric",attributeName:t,name:n,numericValue:n,operator:e})}))}))})),e.tagRefinements.forEach((function(e){r.push({type:"tag",attributeName:"_tags",name:e})})),r},e.exports=v},6571:(e,t,r)=>{"use strict";var n=r(2733),i=r(6732),a=r(2909).escapeFacetValue,s=r(3014),c=r(4728),u=r(317),o=r(1383),h=r(9127),f=r(9228),l=r(3371),m=r(7691),d=r(6938);function p(e,t,r,n){"function"==typeof e.addAlgoliaAgent&&e.addAlgoliaAgent("JS Helper ("+d+")"),this.setClient(e);var i=r||{};i.index=t,this.state=l.make(i),this.recommendState=new h({params:i.recommendState}),this.lastResults=null,this._queryId=0,this._lastQueryIdReceived=-1,this.derivedHelpers=[],this._currentNbQueries=0,this._searchResultsOptions=n}function v(e){if(e<0)throw new Error("Page requested below 0.");return this._change({state:this.state.setPage(e),isPageReset:!1}),this}function g(){return this.state.page}s(p,n),p.prototype.search=function(){return this._search({onlyWithDerivedHelpers:!1}),this},p.prototype.searchOnlyWithDerivedHelpers=function(){return this._search({onlyWithDerivedHelpers:!0}),this},p.prototype.getQuery=function(){var e=this.state;return f._getHitsSearchParams(e)},p.prototype.searchOnce=function(e,t){var r=e?this.state.setQueryParameters(e):this.state,n=f._getQueries(r.index,r),i=this;if(this._currentNbQueries++,this.emit("searchOnce",{state:r}),!t)return this.client.search(n).then((function(e){return i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),{content:new m(r,e.results),state:r,_originalResponse:e}}),(function(e){throw i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),e}));this.client.search(n).then((function(e){i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),t(null,new m(r,e.results),r)})).catch((function(e){i._currentNbQueries--,0===i._currentNbQueries&&i.emit("searchQueueEmpty"),t(e,null,r)}))},p.prototype.findAnswers=function(e){console.warn("[algoliasearch-helper] answers is no longer supported");var t=this.state,r=this.derivedHelpers[0];if(!r)return Promise.resolve([]);var n=r.getModifiedState(t),i=c({attributesForPrediction:e.attributesForPrediction,nbHits:e.nbHits},{params:o(f._getHitsSearchParams(n),["attributesToSnippet","hitsPerPage","restrictSearchableAttributes","snippetEllipsisText"])}),a="search for answers was called, but this client does not have a function client.initIndex(index).findAnswers";if("function"!=typeof this.client.initIndex)throw new Error(a);var s=this.client.initIndex(n.index);if("function"!=typeof s.findAnswers)throw new Error(a);return s.findAnswers(n.query,e.queryLanguages,i)},p.prototype.searchForFacetValues=function(e,t,r,n){var i="function"==typeof this.client.searchForFacetValues,s="function"==typeof this.client.initIndex;if(!i&&!s&&"function"!=typeof this.client.search)throw new Error("search for facet values (searchable) was called, but this client does not have a function client.searchForFacetValues or client.initIndex(index).searchForFacetValues");var c=this.state.setQueryParameters(n||{}),u=c.isDisjunctiveFacet(e),o=f.getSearchForFacetQuery(e,t,r,c);this._currentNbQueries++;var h,l=this;return i?h=this.client.searchForFacetValues([{indexName:c.index,params:o}]):s?h=this.client.initIndex(c.index).searchForFacetValues(o):(delete o.facetName,h=this.client.search([{type:"facet",facet:e,indexName:c.index,params:o}]).then((function(e){return 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-

· 3 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

As we introduced in our previous blog post [25], the term Maximal Extractable Value (MEV) refers to the highest amount of value that can be extracted from a blockchain by a producer/validator during the process of creating a block. This can be achieved by including, excluding, or rearranging the order of transactions.

MEV is a relatively novel topic with many unexplored security consequences. For instance, on April 3rd, 2023, a malicious validator node operator exploited a vulnerability in the mev-boost-relay [26], the most popular MEV relay, to get access to the transaction bundle content, even when the block was created to fail the validation to be accepted by the beacon chain nodes. With the obtained list of transactions, the attacker could execute a front-running attack, earning 20 million USD from the victim block bundle builders.

The following graphic explains how the normal flow of block creation in Ethereum among validators, builders, and relays should work [27]:

Typically, block proposers (consensus validators) should blindly sign block headers created by block builders and then wait until the blocks are already announced to the network to be able to access the block content (the transactions). Hence, the proposers cannot steal the MEV created by the block builders. However, a vulnerability in the relay software allowed a malicious proposer to modify a block header with corrupt data that the network should not accept.q The relay assumed the signed block as valid and returned the block content to the proposer, which instead extracted the MEV for itself. It then created another valid block that was accepted by the network, therefore stealing the builder's profit.

Here [28], you can see the vulnerability disclosure write-up with a more detailed explanation and countermeasures taken by the developers. One of the mentioned countermeasures is that the relay does not send the block content directly to the block proposer; instead, it has to read it from the network when it's accepted. This approach is only partially practical because a malicious proposer could run many validator nodes or even launch an eclipse attack on the network so it can see the block's content much faster and try to front-run the relay. Tikuna may help detect such eclipse attacks and alert the affected relays.

As we see here, malicious validators are becoming a worrying reality. Monitoring the behavior of validators can help identify those trying to take advantage of the blockchain network. At Sakundi [21], we are developing technologies to monitor the blockchain using AI technologies that may help detect such malicious validators.

In the upcoming series of posts, we will be exploring other topics related to MEV in the context of blockchain technology. Stay tuned for more!

Have questions? Contact us today to find out more about our services.

· 11 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

¡Estamos encantados de anunciar la finalización de la primera fase de nuestro proyecto Tikuna! Después de meses de arduo trabajo, dedicación y colaboración, nos complace compartir los resultados de la primera ronda de la beca de investigación académica de la Fundación Ethereum (EF). Destacaremos los hitos de nuestro proyecto y examinaremos los hallazgos de nuestro equipo en este blog.

Descripción General

La seguridad en blockchain se está volviendo cada vez más relevante en el ciberespacio actual y por eso es importante fortalecer la seguridad de cada capa de su arquitectura. Nuestro proyecto se centra en las capas más bajas del blockchain, en particular en la red P2P que permite que los nodos se comuniquen entre sí y compartan información. Esta capa es de vital importancia para cualquier blockchain, incluido Ethereum, debido a la naturaleza descentralizada de su arquitectura. No obstante, la capa de red P2P puede ser vulnerable a varios ataques, como una Denegación de Servicio Distribuida (DDoS), ataques de eclipse y ataques Sybil. En conclusión, esta capa es propensa a muchas amenazas heredadas de las redes P2P, y existe la necesidad de analizarlas y comprenderlas recopilando datos y extrayendo información del comportamiento de la red para reducir los riesgos asociados a estos ataques. Nosotros presentamos Tikuna, una herramienta de código abierto para monitorear y detectar posibles ataques a la red blockchain de Ethereum, en una etapa temprana del ataque. Tikuna usa un método de memoria a corto plazo no supervisado (LSTM por sus siglas en inglés), basado en redes neuronales recurrentes (RRN) para la detección de anomalías asociadas a posibles ataques en la red P2P. Los resultados empíricos obtenidos, indican que el algoritmo propuesto mejora significativamente el rendimiento de detección de ataques, con la capacidad de encontrarlos y clasificarlos, incluyendo ataques Eclipse, ataques Covert Flash y de otro tipo dirigidos a la capa P2P del blockchain Ethereum, con alta precisión.

Nuestro proyecto tenía como objetivo implementar Tikuna, una Prueba de Concepto, para un sistema de monitoreo de seguridad, del blockchain Ethereum. Con Tikuna nuestro objetivo era mejorar la visibilidad del estado de la red P2P. El proyecto se compone de tres entregables:

  1. Desarrollo de una solución de monitoreo P2P de código abierto accesible para la comunidad de EF.
  2. Creación de dashboards para visualizar diferentes aspectos de monitoreo de la red P2P.
  3. El borrador de un paper de investigación para presentar los hallazgos científicos de nuestro proyecto Tikuna.

Tikuna, el primer proyecto en América Latina en obtener fondos de investigación académica de la Fundación Ethereum, ha creado una estrategia novedosa para aumentar la conciencia sobre el estado de seguridad de la red P2P. Estamos orgullosos de contribuir al avance de la seguridad del blockchain y mejorar la seguridad del blockchain de Ethereum. Para obtener más información sobre Tikuna, visite nuestro sitio web [19].

Roadmap de la Fase I

Hallazgos significativos

Presentamos tres contribuciones principales:

  • Nuestro algoritmo propuesto de detección de anomalías de aprendizaje automático, puede detectar varios ataques en la capa P2P de Ethereum utilizando datos de seguimiento de eventos de nodos en un entorno de simulación.

  • Demostramos cómo detectar ataques de eclipse en un nodo en la red principal mediante la extracción de datos de registro de conexión generados de forma personalizada del cliente Ethereum Prysm y el uso de una red neuronal LSTM.

  • Desarrollamos un exploit personalizado que implementa un ataque de eclipse en el mundo real. Se probó con un cliente Prysm modificado en la red principal, por lo que no reenvía spam a la red. Con esto, podríamos probar la efectividad de nuestro algoritmo.

El paso inicial de nuestro equipo fue realizar una investigación y un análisis en profundidad para obtener una comprensión completa de los diversos algoritmos de seguridad P2P existentes, es decir, el estado del arte. En base a esto, desarrollamos un plan de acción integral que describe nuestro algoritmo y metodología en el proyecto Tikuna.

Posteriormente, procedimos a implementar un algoritmo basado en LSTM para el monitoreo de la seguridad y la detección de anomalías de la red blockchain de Ethereum. Además, nuestro equipo desarrolló e implementó tres dashboards destinados a mejorar la seguridad de la red P2P. Estos dashboards son: el de estado de la red P2P de Ethereum, el dashboard para el monitoreo de nodos Beacon y el dashboard de ataques de Eclipse, los cuales describiremos más adelante.

Finalmente, a lo largo del proceso de desarrollo, probamos rigurosamente nuestro algoritmo para asegurarnos de que fuera funcional. También llevamos a cabo varios experimentos en dos entornos de red distintos: el ambiente de pruebas simulado y la red principal de Ethereum, para evaluar minuciosamente la eficacia y el rendimiento del algoritmo de Tikuna.

La siguiente es una muestra de los datos de entrenamiento utilizados para Tikuna en la red principal de Ethereum, que consta de datos de conexión de descubrimiento (UDP) de nodos atacantes normales y nodos atacantes simulados. Los datos normales se recopilaron de varios nodos durante 3 días en funcionamiento normal, y los datos maliciosos se recopilaron de un solo nodo Ethereum víctima del atacado simulado, utilizando nuestro exploit. Cada línea tiene varias funciones de entrada, incluida la marca de tiempo, la IP y el puerto eliminados de la tabla de nodos, la IP y el puerto agregados a la tabla de nodos y el bucket donde se agrega el nodo.

El model y las simulaciones

Nosotros hemos utilizado RNN's para nuestra investigación. Son un tipo de modelo frecuentemente utilizado para procesar datos secuenciales como series de tiempo. Estos modelos están especializados en procesar una secuencia de valores en función del tiempo. Los RNN's pueden escalar a largas secuencias que no serían prácticas para redes sin especialización basada en secuencias. La mayoría de las redes recurrentes también pueden procesar secuencias de longitud variable.

Uno de estos modelos es de especial interés para nuestra investigación. El modelo de memoria a corto plazo largo (LSTM) utiliza un mecanismo de puerta para garantizar la propagación adecuada de la información a través de muchas iteraciones en el tiempo. Las redes LSTM tienen una celda de memoria específica y pueden capturar dependencias a largo plazo en datos secuenciales. LSTM son herramientas valiosas para problemas de modelado de lenguaje. Las redes LSTM son una versión de las redes neuronales recurrentes útiles para largas secuencias de datos interrelacionadas. LSTM fue elegido en esta investigación para la detección de anomalías para encontrar conexiones maliciosas en la comunicación con otros nodos de un cliente de Ethereum.

A continuación se muestra un ejemplo de la salida del modelo de ML para la red principal donde obtuvimos los mejores resultados detectando ataques de eclipse, con 1’000,000 de líneas de registro de conexión normales para entrenamiento y alrededor de 3,000 líneas de registros de ataques de eclipse para su evaluación:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

Nostros utilizamos métricas de medidas estándar para la detección de intrusiones con ML, como f1, recuperación, precisión y exactitud. Revise el repositorio de Github y esté atento a la publicación de nuestro borrador del paper de investigación si desea obtener más detalles.

Dashboards

Como parte de nuestra investigación, nuestro equipo consideró importante mostrar los resultados obtenidos, así como una posible implementación. Para lograr esto, creamos tres dashboards de Grafana que están integrados con Prometheus. Estos dashboards capturan métricas de monitoreo y muestran el comportamiento de la red P2P, el vecindario y el nodo Prysm en tiempo real.

  • Estado de la red P2P Ethereum: La información que se muestra en el dashboard del estado de la red P2P Ethereum proviene de nuestro nodo Prysm y se obtiene al monitorear el puerto 8080. Brinda datos sobre el vecindario de nuestro nodo y cómo este interactúa con los otros nodos del Blockchain. Este muestra la cantidad de pares conectados, el socket actual y el principal, los diversos tipos de bibliotecas P2P que están conectados a nuestro nodo junto con sus calificaciones, así como información sobre la tasa de participación y la vitalidad de la red P2P. Además de esto, brinda información sobre los nodos validadores, como su número total, el estado de sus cuentas y la cantidad promedio de ether en sus saldos.

Img. 1 El dashboard del estado de la red P2P Ethereum

  • Beacon Node Monitor: Este dashboard está relacionado a la infraestructura de nuestro nodo de consenso Prysm, muestra información como el uso de CPU, RAM, espacio en disco, consumo de ancho de banda y detalles sobre el comportamiento de los discos. Esta información se recuperó del clúster de Kubernetes que sirve como host para nuestro nodo de consenso, donde reside actualmente.

Img. 2 El dashboard de monitoreo del nodo Beacon

  • Eclipse Attacks Dashboard: La creación del tablero final requirió el desarrollo de una interfaz que pudiera registrar los posibles ataques de eclipse descubiertos por nuestro algoritmo de aprendizaje automático y luego ingresar esos registros en el componente AlertManager de Prometheus. Después de eso, Grafana crea una tabla que muestra una fila para cada ataque de eclipse identificado en un nodo determinado.

Img. 3 El dashboard de ataques de eclipse

En Tikuna, creemos que hacer una red P2P más segura y confiable requiere accesibilidad y transparencia, por lo que hemos desarrollado una sección de usuario integral para nuestra prueba de concepto. Nuestra sección de usuario ofrece instrucciones detalladas y tutoriales que guían a los usuarios de todos los niveles técnicos a través del proceso de instalación y explican cómo interpretar la información presentada en los dashboards. Con Tikuna, una audiencia más amplia puede beneficiarse de nuestro sistema de monitoreo P2P. Estamos orgullosos de contribuir al avance de la seguridad de blockchain a través de nuestras ideas innovadoras y compromiso con soluciones fáciles de usar. Para obtener más información sobre Tikuna y cómo usar nuestro sistema de monitoreo, visite nuestro sitio web [19] y navegue a través de nuestra sección de usuario.

El borrador del paper de investigación

Actualmente estamos trabajando en un borrador de un paper de investigación que se basa en los hallazgos y resultados obtenidos durante nuestra investigación. Una vez que recibamos los comentarios de EF, tenemos la intención de enviar el paper de investigación a una conferencia internacional. The paper de investigación está organizado en cinco capítulos principales. +

· 3 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

As we introduced in our previous blog post [25], the term Maximal Extractable Value (MEV) refers to the highest amount of value that can be extracted from a blockchain by a producer/validator during the process of creating a block. This can be achieved by including, excluding, or rearranging the order of transactions.

MEV is a relatively novel topic with many unexplored security consequences. For instance, on April 3rd, 2023, a malicious validator node operator exploited a vulnerability in the mev-boost-relay [26], the most popular MEV relay, to get access to the transaction bundle content, even when the block was created to fail the validation to be accepted by the beacon chain nodes. With the obtained list of transactions, the attacker could execute a front-running attack, earning 20 million USD from the victim block bundle builders.

The following graphic explains how the normal flow of block creation in Ethereum among validators, builders, and relays should work [27]:

Typically, block proposers (consensus validators) should blindly sign block headers created by block builders and then wait until the blocks are already announced to the network to be able to access the block content (the transactions). Hence, the proposers cannot steal the MEV created by the block builders. However, a vulnerability in the relay software allowed a malicious proposer to modify a block header with corrupt data that the network should not accept.q The relay assumed the signed block as valid and returned the block content to the proposer, which instead extracted the MEV for itself. It then created another valid block that was accepted by the network, therefore stealing the builder's profit.

Here [28], you can see the vulnerability disclosure write-up with a more detailed explanation and countermeasures taken by the developers. One of the mentioned countermeasures is that the relay does not send the block content directly to the block proposer; instead, it has to read it from the network when it's accepted. This approach is only partially practical because a malicious proposer could run many validator nodes or even launch an eclipse attack on the network so it can see the block's content much faster and try to front-run the relay. Tikuna may help detect such eclipse attacks and alert the affected relays.

As we see here, malicious validators are becoming a worrying reality. Monitoring the behavior of validators can help identify those trying to take advantage of the blockchain network. At Sakundi [21], we are developing technologies to monitor the blockchain using AI technologies that may help detect such malicious validators.

In the upcoming series of posts, we will be exploring other topics related to MEV in the context of blockchain technology. Stay tuned for more!

Have questions? Contact us today to find out more about our services.

· 11 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

¡Estamos encantados de anunciar la finalización de la primera fase de nuestro proyecto Tikuna! Después de meses de arduo trabajo, dedicación y colaboración, nos complace compartir los resultados de la primera ronda de la beca de investigación académica de la Fundación Ethereum (EF). Destacaremos los hitos de nuestro proyecto y examinaremos los hallazgos de nuestro equipo en este blog.

Descripción General

La seguridad en blockchain se está volviendo cada vez más relevante en el ciberespacio actual y por eso es importante fortalecer la seguridad de cada capa de su arquitectura. Nuestro proyecto se centra en las capas más bajas del blockchain, en particular en la red P2P que permite que los nodos se comuniquen entre sí y compartan información. Esta capa es de vital importancia para cualquier blockchain, incluido Ethereum, debido a la naturaleza descentralizada de su arquitectura. No obstante, la capa de red P2P puede ser vulnerable a varios ataques, como una Denegación de Servicio Distribuida (DDoS), ataques de eclipse y ataques Sybil. En conclusión, esta capa es propensa a muchas amenazas heredadas de las redes P2P, y existe la necesidad de analizarlas y comprenderlas recopilando datos y extrayendo información del comportamiento de la red para reducir los riesgos asociados a estos ataques. Nosotros presentamos Tikuna, una herramienta de código abierto para monitorear y detectar posibles ataques a la red blockchain de Ethereum, en una etapa temprana del ataque. Tikuna usa un método de memoria a corto plazo no supervisado (LSTM por sus siglas en inglés), basado en redes neuronales recurrentes (RRN) para la detección de anomalías asociadas a posibles ataques en la red P2P. Los resultados empíricos obtenidos, indican que el algoritmo propuesto mejora significativamente el rendimiento de detección de ataques, con la capacidad de encontrarlos y clasificarlos, incluyendo ataques Eclipse, ataques Covert Flash y de otro tipo dirigidos a la capa P2P del blockchain Ethereum, con alta precisión.

Nuestro proyecto tenía como objetivo implementar Tikuna, una Prueba de Concepto, para un sistema de monitoreo de seguridad, del blockchain Ethereum. Con Tikuna nuestro objetivo era mejorar la visibilidad del estado de la red P2P. El proyecto se compone de tres entregables:

  1. Desarrollo de una solución de monitoreo P2P de código abierto accesible para la comunidad de EF.
  2. Creación de dashboards para visualizar diferentes aspectos de monitoreo de la red P2P.
  3. El borrador de un paper de investigación para presentar los hallazgos científicos de nuestro proyecto Tikuna.

Tikuna, el primer proyecto en América Latina en obtener fondos de investigación académica de la Fundación Ethereum, ha creado una estrategia novedosa para aumentar la conciencia sobre el estado de seguridad de la red P2P. Estamos orgullosos de contribuir al avance de la seguridad del blockchain y mejorar la seguridad del blockchain de Ethereum. Para obtener más información sobre Tikuna, visite nuestro sitio web [19].

Roadmap de la Fase I

Hallazgos significativos

Presentamos tres contribuciones principales:

  • Nuestro algoritmo propuesto de detección de anomalías de aprendizaje automático, puede detectar varios ataques en la capa P2P de Ethereum utilizando datos de seguimiento de eventos de nodos en un entorno de simulación.

  • Demostramos cómo detectar ataques de eclipse en un nodo en la red principal mediante la extracción de datos de registro de conexión generados de forma personalizada del cliente Ethereum Prysm y el uso de una red neuronal LSTM.

  • Desarrollamos un exploit personalizado que implementa un ataque de eclipse en el mundo real. Se probó con un cliente Prysm modificado en la red principal, por lo que no reenvía spam a la red. Con esto, podríamos probar la efectividad de nuestro algoritmo.

El paso inicial de nuestro equipo fue realizar una investigación y un análisis en profundidad para obtener una comprensión completa de los diversos algoritmos de seguridad P2P existentes, es decir, el estado del arte. En base a esto, desarrollamos un plan de acción integral que describe nuestro algoritmo y metodología en el proyecto Tikuna.

Posteriormente, procedimos a implementar un algoritmo basado en LSTM para el monitoreo de la seguridad y la detección de anomalías de la red blockchain de Ethereum. Además, nuestro equipo desarrolló e implementó tres dashboards destinados a mejorar la seguridad de la red P2P. Estos dashboards son: el de estado de la red P2P de Ethereum, el dashboard para el monitoreo de nodos Beacon y el dashboard de ataques de Eclipse, los cuales describiremos más adelante.

Finalmente, a lo largo del proceso de desarrollo, probamos rigurosamente nuestro algoritmo para asegurarnos de que fuera funcional. También llevamos a cabo varios experimentos en dos entornos de red distintos: el ambiente de pruebas simulado y la red principal de Ethereum, para evaluar minuciosamente la eficacia y el rendimiento del algoritmo de Tikuna.

La siguiente es una muestra de los datos de entrenamiento utilizados para Tikuna en la red principal de Ethereum, que consta de datos de conexión de descubrimiento (UDP) de nodos atacantes normales y nodos atacantes simulados. Los datos normales se recopilaron de varios nodos durante 3 días en funcionamiento normal, y los datos maliciosos se recopilaron de un solo nodo Ethereum víctima del atacado simulado, utilizando nuestro exploit. Cada línea tiene varias funciones de entrada, incluida la marca de tiempo, la IP y el puerto eliminados de la tabla de nodos, la IP y el puerto agregados a la tabla de nodos y el bucket donde se agrega el nodo.

El model y las simulaciones

Nosotros hemos utilizado RNN's para nuestra investigación. Son un tipo de modelo frecuentemente utilizado para procesar datos secuenciales como series de tiempo. Estos modelos están especializados en procesar una secuencia de valores en función del tiempo. Los RNN's pueden escalar a largas secuencias que no serían prácticas para redes sin especialización basada en secuencias. La mayoría de las redes recurrentes también pueden procesar secuencias de longitud variable.

Uno de estos modelos es de especial interés para nuestra investigación. El modelo de memoria a corto plazo largo (LSTM) utiliza un mecanismo de puerta para garantizar la propagación adecuada de la información a través de muchas iteraciones en el tiempo. Las redes LSTM tienen una celda de memoria específica y pueden capturar dependencias a largo plazo en datos secuenciales. LSTM son herramientas valiosas para problemas de modelado de lenguaje. Las redes LSTM son una versión de las redes neuronales recurrentes útiles para largas secuencias de datos interrelacionadas. LSTM fue elegido en esta investigación para la detección de anomalías para encontrar conexiones maliciosas en la comunicación con otros nodos de un cliente de Ethereum.

A continuación se muestra un ejemplo de la salida del modelo de ML para la red principal donde obtuvimos los mejores resultados detectando ataques de eclipse, con 1’000,000 de líneas de registro de conexión normales para entrenamiento y alrededor de 3,000 líneas de registros de ataques de eclipse para su evaluación:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

Nostros utilizamos métricas de medidas estándar para la detección de intrusiones con ML, como f1, recuperación, precisión y exactitud. Revise el repositorio de Github y esté atento a la publicación de nuestro borrador del paper de investigación si desea obtener más detalles.

Dashboards

Como parte de nuestra investigación, nuestro equipo consideró importante mostrar los resultados obtenidos, así como una posible implementación. Para lograr esto, creamos tres dashboards de Grafana que están integrados con Prometheus. Estos dashboards capturan métricas de monitoreo y muestran el comportamiento de la red P2P, el vecindario y el nodo Prysm en tiempo real.

  • Estado de la red P2P Ethereum: La información que se muestra en el dashboard del estado de la red P2P Ethereum proviene de nuestro nodo Prysm y se obtiene al monitorear el puerto 8080. Brinda datos sobre el vecindario de nuestro nodo y cómo este interactúa con los otros nodos del Blockchain. Este muestra la cantidad de pares conectados, el socket actual y el principal, los diversos tipos de bibliotecas P2P que están conectados a nuestro nodo junto con sus calificaciones, así como información sobre la tasa de participación y la vitalidad de la red P2P. Además de esto, brinda información sobre los nodos validadores, como su número total, el estado de sus cuentas y la cantidad promedio de ether en sus saldos.

Img. 1 El dashboard del estado de la red P2P Ethereum

  • Beacon Node Monitor: Este dashboard está relacionado a la infraestructura de nuestro nodo de consenso Prysm, muestra información como el uso de CPU, RAM, espacio en disco, consumo de ancho de banda y detalles sobre el comportamiento de los discos. Esta información se recuperó del clúster de Kubernetes que sirve como host para nuestro nodo de consenso, donde reside actualmente.

Img. 2 El dashboard de monitoreo del nodo Beacon

  • Eclipse Attacks Dashboard: La creación del tablero final requirió el desarrollo de una interfaz que pudiera registrar los posibles ataques de eclipse descubiertos por nuestro algoritmo de aprendizaje automático y luego ingresar esos registros en el componente AlertManager de Prometheus. Después de eso, Grafana crea una tabla que muestra una fila para cada ataque de eclipse identificado en un nodo determinado.

Img. 3 El dashboard de ataques de eclipse

En Tikuna, creemos que hacer una red P2P más segura y confiable requiere accesibilidad y transparencia, por lo que hemos desarrollado una sección de usuario integral para nuestra prueba de concepto. Nuestra sección de usuario ofrece instrucciones detalladas y tutoriales que guían a los usuarios de todos los niveles técnicos a través del proceso de instalación y explican cómo interpretar la información presentada en los dashboards. Con Tikuna, una audiencia más amplia puede beneficiarse de nuestro sistema de monitoreo P2P. Estamos orgullosos de contribuir al avance de la seguridad de blockchain a través de nuestras ideas innovadoras y compromiso con soluciones fáciles de usar. Para obtener más información sobre Tikuna y cómo usar nuestro sistema de monitoreo, visite nuestro sitio web [19] y navegue a través de nuestra sección de usuario.

El borrador del paper de investigación

Actualmente estamos trabajando en un borrador de un paper de investigación que se basa en los hallazgos y resultados obtenidos durante nuestra investigación. Una vez que recibamos los comentarios de EF, tenemos la intención de enviar el paper de investigación a una conferencia internacional. The paper de investigación está organizado en cinco capítulos principales. El primer capítulo es una introducción a la cadena de bloques de Ethereum, que enfatiza la importancia de comprender los riesgos asociados con las redes P2P de Blockchain y desarrollar soluciones centradas en la seguridad para garantizar su confiabilidad. También destaca las tres contribuciones principales del proyecto, que incluyen el algoritmo de aprendizaje automático propuesto para detectar ataques en la capa P2P, un método para detectar ataques de eclipse utilizando datos de registro de conexión generados de forma personalizada y redes neuronales LSTM, y un exploit personalizado para ejecutar ataques de eclipse. El segundo capítulo, titulado "Trabajos relacionados", ofrece una descripción general de los trabajos más recientes que abordan los desafíos de seguridad de las redes P2P de blockchain de Ethereum. El tercer capítulo presenta el algoritmo de Tikuna describiendo cada uno de los tres pasos y discutiendo los diversos tipos de ataques a la red P2P de blockchain. El cuarto capítulo evalúa la efectividad del algoritmo de Tikuna utilizando un conjunto de datos de simulación y de conexión a la red principal. El último capítulo presenta un resumen del trabajo propuesto, las conclusiones y las posibles direcciones futuras de investigación. Finalmente, los autores reconocen que el trabajo presentado fue apoyado por las becas de investigación del EF.

Siguientes pasos

Nos enorgullecemos enormemente de los logros de nuestro equipo y extendemos nuestra gratitud a todos los que contribuyeron a garantizar el éxito del proyecto. La experiencia, la dedicación y el espíritu colaborativo de nuestro equipo fueron cruciales para entregar la primera fase del proyecto Tikuna. Este es solo el comienzo de nuestro proyecto, ya que nuestro objetivo es desarrollar Tikuna como una herramienta para contribuir a la comunidad Ethereum y otras Blockchains. Nuestros esfuerzos se centrarán en identificar ataques adicionales, minimizar los falsos positivos, detectar incidentes del mundo real e incorporar diferentes clientes de nodos de Blockchain (incluido Ethereum). Además, tenemos la intención de investigar otras áreas donde se puede utilizar Tikuna, como el valor máximo extraíble (MEV).

Hemos solicitado la segunda ronda de las becas de investigación académicas del EF. Consulte nuestra aplicación aquí. Si está interesado en aprender más sobre Tikuna o trabajar con nosotros, agradecemos la oportunidad de colaborar con usted. Comuníquese con nosotros y estaremos encantados de saber de usted.

· 2 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Aunque existen avances en materia de ciberseguridad, muchas aplicaciones no cuentan con sólidas funciones de seguridad. Actualmente, más allá de razones de calidad del producto, el software tiene muchos enemigos detrás que buscan romper las líneas para aprovecharse o sabotear las condiciones del software.

Dentro de la Web 3 es la misma historia o quizás aún más grave, porque están implicados elementos como los activos digitales (criptomonedas o NFT). Aunque estas tecnologías utilizan blockchain y su algoritmo de consenso como base para llevar a cabo los procesos backend, tienen decenas de agujeros que son descubiertos con el tiempo por hackers, cuyo objetivo no es contribuir al ecosistema. Imagina una gran red descentralizada donde cientos de proyectos innovadores o emergentes buscan utilizarla para el desarrollo de sus aplicaciones, este es un escenario existente que sucede dentro de la blockchain de Ethereum. En la actualidad, los crecientes niveles de hackeos, persecución de fondos o descuidos por parte de instituciones, como la última emisión de FTX, han puesto a la ciberseguridad del blockchain como el refugio detrás de la perfección de sus operaciones. Los exchanges, los contratos inteligentes, los tokens o las aplicaciones descentralizadas forman parte de los elementos que son auditados para verificar su calidad y seguridad. 156 millones de dólares fueron robados en apenas cuatro meses en 2021 en hackeos a protocolos de finanzas descentralizadas, más allá de la cantidad que representa una pérdida significativa para particulares y empresas, también es una señal a nivel cualitativo, que demuestra que los protocolos requieren integración y mantenimiento en materia de seguridad informática.

Desde el equipo de Tikuna reconocemos esto, de ahí que estemos haciendo una importante contribución a la comunidad y a la ciberseguridad en blockchain, desarrollando y ofreciendo monitorización de detección temprana de ataques para la red P2P Ethereum. Contar con sistemas que puedan reforzar y monitorizar entornos descentralizados, antes de que ocurran estos desafortunados eventos, genera más confianza y una intensa propiedad anticipatoria. Como solución de código abierto, los usuarios podrán aprovechar los datos expuestos, con la intención de complementar la integración de la seguridad y hacer que el consumo de productos blockchain, y dentro de Ethereum, sea cada vez más seguro y predecible, características ambas muy valoradas en la actualidad.

· 3 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Los sistemas de seguridad informática son complejos y juegan un papel importante en el desarrollo de aplicaciones. En sistemas tan seguros y descentralizados como redes blockchain, también existen casos en que las dApps, personas usuarias e incluso la misma red se encuentran expuestas a muchos riesgos todos los días, riesgos que pueden vulnerar elementos importantes de manera individual o colectiva. Es por esto que muchas organizaciones se encuentran interesadas en promover la investigación y el desarrollo en soluciones que velen por la seguridad, por lo que precisamente estamos desarrollando Tikuna.

Tikuna es una prueba de concepto de un sistema de monitorización de la seguridad de la red P2P para el blockchain de Ethereum. Al implementar técnicas de machine learning, Tikuna extraerá información sobre seguridad y rendimiento para la detección temprana de incidentes desafortunados. Y mejorando la visibilidad al estado de seguridad de la red P2P. Este proyecto de investigación propuesto ha sido aprobado y apoyado por la Ethereum Foundation con una donación de USD 102.200 para iniciar el proceso de desarrollo.

El equipo de Tikuna se ha acercado este 2022 a la apertura de los Grants académicos de Ethereum Foundation, en el área de P2P Network. Somos el único equipo en América Latina que recibió la aprobación y financiación de Ethereum Foundation. Este es el anuncio oficial publicado por ellos.

¿Quién es el equipo detrás?

team

Tikuna es un proyecto liderado por Edenia y Sakundi. Edenia gestiona una infraestructura independiente de blockchain y desarrolla soluciones Web3. Sakundi es una organización enfocada en la seguridad y privacidad de blockchain basada en la investigación y que ofrece soluciones de monitorización de seguridad para organizaciones blockchain y aplicaciones distribuidas basadas en herramientas de Inteligencia Artificial.

¿Cuál es el problema que Tikuna trata de resolver?

Tikuna propone una solución de código abierto que ayuda a aumentar la visibilidad en el estado de los nodos, detectando de manera temprana ataques potenciales. Este es un hecho que aporta a la comunidad una herramienta adicional para prevenir o reportar incidentes que terminan siendo un suceso desafortunado, afectando miles de transacciones y/o aplicaciones que funcionan sobre esta blockchain.

¿Cómo se dispondrá la información que Tikuna va a representar?

Dentro de los entregables se encuentra un dashboard interactivo en el que los usuarios u organizaciones pueden consultar información de los nodos. Esta información será de datos abiertos y no tendrá ningún costo o limitante legal para su uso o reproducción. Además, adjunto habrá un documento científico que respalde el producto.

¿Cuál será el resultado final del proyecto de investigación Tikuna, en esta primera etapa?

El objetivo general del proyecto es apoyar a la comunidad de Ethereum proporcionándoles una herramienta inteligente de última generación capaz de recolectar información relacionada con la seguridad del estado de la red P2P y mejorar la visibilidad de la red proporcionando información sobre su estado actual. Un entregable que se pretende desarrollar es un dashboard en el que los usuarios u organizaciones pueden hacer una consulta de los nodos.

Pasos hacia el futuro….

Tikuna es una solución innovadora que dará respaldo a la seguridad blockchain de Ethereum, más aún en la evolución hacia PoS (Proof-of-Stake) en el que los nodos juegan un papel más importante en la validación de bloques con su participación. Con el ritmo acelerado de la expansión de las aplicaciones Web3 y el uso de redes de participación, pensamos que en el futuro Tikuna tiene el potencial de ser una solución a escala multi-chain. Con estos esfuerzos, iremos expandiendo su aplicación a través de redes distintas que usen PoS o DPoS como su mecanismo de consenso.

- - +
+ + \ No newline at end of file diff --git a/es/blog/introducing-tikuna/index.html b/es/blog/introducing-tikuna/index.html index 6ae531d..e5c1900 100644 --- a/es/blog/introducing-tikuna/index.html +++ b/es/blog/introducing-tikuna/index.html @@ -3,24 +3,20 @@ -Tikuna: Seguridad en Blockchain usando IA - Apoyado por Ethereum Foundation. | Blockchain Security Monitoring - - - - - - - +Tikuna: Seguridad en Blockchain usando IA - Apoyado por Ethereum Foundation. | Blockchain Security Monitoring + + +
-

Tikuna: Seguridad en Blockchain usando IA - Apoyado por Ethereum Foundation.

· 3 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Los sistemas de seguridad informática son complejos y juegan un papel importante en el desarrollo de aplicaciones. En sistemas tan seguros y descentralizados como redes blockchain, también existen casos en que las dApps, personas usuarias e incluso la misma red se encuentran expuestas a muchos riesgos todos los días, riesgos que pueden vulnerar elementos importantes de manera individual o colectiva. Es por esto que muchas organizaciones se encuentran interesadas en promover la investigación y el desarrollo en soluciones que velen por la seguridad, por lo que precisamente estamos desarrollando Tikuna.

Tikuna es una prueba de concepto de un sistema de monitorización de la seguridad de la red P2P para el blockchain de Ethereum. Al implementar técnicas de machine learning, Tikuna extraerá información sobre seguridad y rendimiento para la detección temprana de incidentes desafortunados. Y mejorando la visibilidad al estado de seguridad de la red P2P. Este proyecto de investigación propuesto ha sido aprobado y apoyado por la Ethereum Foundation con una donación de USD 102.200 para iniciar el proceso de desarrollo.

El equipo de Tikuna se ha acercado este 2022 a la apertura de los Grants académicos de Ethereum Foundation, en el área de P2P Network. Somos el único equipo en América Latina que recibió la aprobación y financiación de Ethereum Foundation. Este es el anuncio oficial publicado por ellos.

¿Quién es el equipo detrás?

team

Tikuna es un proyecto liderado por Edenia y Sakundi. Edenia gestiona una infraestructura independiente de blockchain y desarrolla soluciones Web3. Sakundi es una organización enfocada en la seguridad y privacidad de blockchain basada en la investigación y que ofrece soluciones de monitorización de seguridad para organizaciones blockchain y aplicaciones distribuidas basadas en herramientas de Inteligencia Artificial.

¿Cuál es el problema que Tikuna trata de resolver?

Tikuna propone una solución de código abierto que ayuda a aumentar la visibilidad en el estado de los nodos, detectando de manera temprana ataques potenciales. Este es un hecho que aporta a la comunidad una herramienta adicional para prevenir o reportar incidentes que terminan siendo un suceso desafortunado, afectando miles de transacciones y/o aplicaciones que funcionan sobre esta blockchain.

¿Cómo se dispondrá la información que Tikuna va a representar?

Dentro de los entregables se encuentra un dashboard interactivo en el que los usuarios u organizaciones pueden consultar información de los nodos. Esta información será de datos abiertos y no tendrá ningún costo o limitante legal para su uso o reproducción. Además, adjunto habrá un documento científico que respalde el producto.

¿Cuál será el resultado final del proyecto de investigación Tikuna, en esta primera etapa?

El objetivo general del proyecto es apoyar a la comunidad de Ethereum proporcionándoles una herramienta inteligente de última generación capaz de recolectar información relacionada con la seguridad del estado de la red P2P y mejorar la visibilidad de la red proporcionando información sobre su estado actual. +

Tikuna: Seguridad en Blockchain usando IA - Apoyado por Ethereum Foundation.

· 3 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Los sistemas de seguridad informática son complejos y juegan un papel importante en el desarrollo de aplicaciones. En sistemas tan seguros y descentralizados como redes blockchain, también existen casos en que las dApps, personas usuarias e incluso la misma red se encuentran expuestas a muchos riesgos todos los días, riesgos que pueden vulnerar elementos importantes de manera individual o colectiva. Es por esto que muchas organizaciones se encuentran interesadas en promover la investigación y el desarrollo en soluciones que velen por la seguridad, por lo que precisamente estamos desarrollando Tikuna.

Tikuna es una prueba de concepto de un sistema de monitorización de la seguridad de la red P2P para el blockchain de Ethereum. Al implementar técnicas de machine learning, Tikuna extraerá información sobre seguridad y rendimiento para la detección temprana de incidentes desafortunados. Y mejorando la visibilidad al estado de seguridad de la red P2P. Este proyecto de investigación propuesto ha sido aprobado y apoyado por la Ethereum Foundation con una donación de USD 102.200 para iniciar el proceso de desarrollo.

El equipo de Tikuna se ha acercado este 2022 a la apertura de los Grants académicos de Ethereum Foundation, en el área de P2P Network. Somos el único equipo en América Latina que recibió la aprobación y financiación de Ethereum Foundation. Este es el anuncio oficial publicado por ellos.

¿Quién es el equipo detrás?

team

Tikuna es un proyecto liderado por Edenia y Sakundi. Edenia gestiona una infraestructura independiente de blockchain y desarrolla soluciones Web3. Sakundi es una organización enfocada en la seguridad y privacidad de blockchain basada en la investigación y que ofrece soluciones de monitorización de seguridad para organizaciones blockchain y aplicaciones distribuidas basadas en herramientas de Inteligencia Artificial.

¿Cuál es el problema que Tikuna trata de resolver?

Tikuna propone una solución de código abierto que ayuda a aumentar la visibilidad en el estado de los nodos, detectando de manera temprana ataques potenciales. Este es un hecho que aporta a la comunidad una herramienta adicional para prevenir o reportar incidentes que terminan siendo un suceso desafortunado, afectando miles de transacciones y/o aplicaciones que funcionan sobre esta blockchain.

¿Cómo se dispondrá la información que Tikuna va a representar?

Dentro de los entregables se encuentra un dashboard interactivo en el que los usuarios u organizaciones pueden consultar información de los nodos. Esta información será de datos abiertos y no tendrá ningún costo o limitante legal para su uso o reproducción. Además, adjunto habrá un documento científico que respalde el producto.

¿Cuál será el resultado final del proyecto de investigación Tikuna, en esta primera etapa?

El objetivo general del proyecto es apoyar a la comunidad de Ethereum proporcionándoles una herramienta inteligente de última generación capaz de recolectar información relacionada con la seguridad del estado de la red P2P y mejorar la visibilidad de la red proporcionando información sobre su estado actual. Un entregable que se pretende desarrollar es un dashboard en el que los usuarios u organizaciones pueden hacer una consulta de los nodos.

Pasos hacia el futuro….

Tikuna es una solución innovadora que dará respaldo a la seguridad blockchain de Ethereum, más aún en la evolución hacia PoS (Proof-of-Stake) en el que los nodos juegan un papel más importante en la validación de bloques con su participación. Con el ritmo acelerado de la expansión de las aplicaciones Web3 y el uso de redes de participación, pensamos que en el futuro Tikuna tiene el potencial de ser una solución a escala multi-chain. Con estos esfuerzos, iremos expandiendo su aplicación a través de redes distintas que usen PoS o DPoS como su mecanismo de consenso.



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+ + \ No newline at end of file diff --git a/es/blog/malicious-validators/index.html b/es/blog/malicious-validators/index.html index ba43830..7940c3b 100644 --- a/es/blog/malicious-validators/index.html +++ b/es/blog/malicious-validators/index.html @@ -3,23 +3,19 @@ -The Danger of Malicious Block Proposers in Maximal Extractable Value | Blockchain Security Monitoring - - - - - - - +The Danger of Malicious Block Proposers in Maximal Extractable Value | Blockchain Security Monitoring + + +
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The Danger of Malicious Block Proposers in Maximal Extractable Value

· 3 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

As we introduced in our previous blog post [25], the term Maximal Extractable Value (MEV) refers to the highest amount of value that can be extracted from a blockchain by a producer/validator during the process of creating a block. This can be achieved by including, excluding, or rearranging the order of transactions.

MEV is a relatively novel topic with many unexplored security consequences. For instance, on April 3rd, 2023, a malicious validator node operator exploited a vulnerability in the mev-boost-relay [26], the most popular MEV relay, to get access to the transaction bundle content, even when the block was created to fail the validation to be accepted by the beacon chain nodes. With the obtained list of transactions, the attacker could execute a front-running attack, earning 20 million USD from the victim block bundle builders.

The following graphic explains how the normal flow of block creation in Ethereum among validators, builders, and relays should work [27]:

Typically, block proposers (consensus validators) should blindly sign block headers created by block builders and then wait until the blocks are already announced to the network to be able to access the block content (the transactions). Hence, the proposers cannot steal the MEV created by the block builders. However, a vulnerability in the relay software allowed a malicious proposer to modify a block header with corrupt data that the network should not accept.q The relay assumed the signed block as valid and returned the block content to the proposer, which instead extracted the MEV for itself. It then created another valid block that was accepted by the network, therefore stealing the builder's profit.

Here [28], you can see the vulnerability disclosure write-up with a more detailed explanation and countermeasures taken by the developers. One of the mentioned countermeasures is that the relay does not send the block content directly to the block proposer; instead, it has to read it from the network when it's accepted. This approach is only partially practical because a malicious proposer could run many validator nodes or even launch an eclipse attack on the network so it can see the block's content much faster and try to front-run the relay. Tikuna may help detect such eclipse attacks and alert the affected relays.

As we see here, malicious validators are becoming a worrying reality. Monitoring the behavior of validators can help identify those trying to take advantage of the blockchain network. At Sakundi [21], we are developing technologies to monitor the blockchain using AI technologies that may help detect such malicious validators.

In the upcoming series of posts, we will be exploring other topics related to MEV in the context of blockchain technology. Stay tuned for more!

Have questions? Contact us today to find out more about our services.



+ + \ No newline at end of file diff --git a/es/blog/tikuna-contribution/index.html b/es/blog/tikuna-contribution/index.html index 236b608..a5d5013 100644 --- a/es/blog/tikuna-contribution/index.html +++ b/es/blog/tikuna-contribution/index.html @@ -3,23 +3,19 @@ -Tikuna - Nuestra contribución a la seguridad en la blockchain | Blockchain Security Monitoring - - - - - - - +Tikuna - Nuestra contribución a la seguridad en la blockchain | Blockchain Security Monitoring + + +
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Tikuna - Nuestra contribución a la seguridad en la blockchain

· 2 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

Aunque existen avances en materia de ciberseguridad, muchas aplicaciones no cuentan con sólidas funciones de seguridad. Actualmente, más allá de razones de calidad del producto, el software tiene muchos enemigos detrás que buscan romper las líneas para aprovecharse o sabotear las condiciones del software.

Dentro de la Web 3 es la misma historia o quizás aún más grave, porque están implicados elementos como los activos digitales (criptomonedas o NFT). Aunque estas tecnologías utilizan blockchain y su algoritmo de consenso como base para llevar a cabo los procesos backend, tienen decenas de agujeros que son descubiertos con el tiempo por hackers, cuyo objetivo no es contribuir al ecosistema. Imagina una gran red descentralizada donde cientos de proyectos innovadores o emergentes buscan utilizarla para el desarrollo de sus aplicaciones, este es un escenario existente que sucede dentro de la blockchain de Ethereum. En la actualidad, los crecientes niveles de hackeos, persecución de fondos o descuidos por parte de instituciones, como la última emisión de FTX, han puesto a la ciberseguridad del blockchain como el refugio detrás de la perfección de sus operaciones. Los exchanges, los contratos inteligentes, los tokens o las aplicaciones descentralizadas forman parte de los elementos que son auditados para verificar su calidad y seguridad. 156 millones de dólares fueron robados en apenas cuatro meses en 2021 en hackeos a protocolos de finanzas descentralizadas, más allá de la cantidad que representa una pérdida significativa para particulares y empresas, también es una señal a nivel cualitativo, que demuestra que los protocolos requieren integración y mantenimiento en materia de seguridad informática.

Desde el equipo de Tikuna reconocemos esto, de ahí que estemos haciendo una importante contribución a la comunidad y a la ciberseguridad en blockchain, desarrollando y ofreciendo monitorización de detección temprana de ataques para la red P2P Ethereum. Contar con sistemas que puedan reforzar y monitorizar entornos descentralizados, antes de que ocurran estos desafortunados eventos, genera más confianza y una intensa propiedad anticipatoria. Como solución de código abierto, los usuarios podrán aprovechar los datos expuestos, con la intención de complementar la integración de la seguridad y hacer que el consumo de productos blockchain, y dentro de Ethereum, sea cada vez más seguro y predecible, características ambas muy valoradas en la actualidad.



+ + \ No newline at end of file diff --git a/es/blog/tikuna-final-blogpost-fase1/index.html b/es/blog/tikuna-final-blogpost-fase1/index.html index c7f54cc..1a2c1c1 100644 --- a/es/blog/tikuna-final-blogpost-fase1/index.html +++ b/es/blog/tikuna-final-blogpost-fase1/index.html @@ -3,25 +3,21 @@ -Anunciando la Entrega de Nuestro Proyecto Tikuna en la Fase de Prueba de Concepto | Blockchain Security Monitoring - - - - - - - +Anunciando la Entrega de Nuestro Proyecto Tikuna en la Fase de Prueba de Concepto | Blockchain Security Monitoring + + +
-

Anunciando la Entrega de Nuestro Proyecto Tikuna en la Fase de Prueba de Concepto

· 11 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

¡Estamos encantados de anunciar la finalización de la primera fase de nuestro proyecto Tikuna! Después de meses de arduo trabajo, dedicación y colaboración, nos complace compartir los resultados de la primera ronda de la beca de investigación académica de la Fundación Ethereum (EF). Destacaremos los hitos de nuestro proyecto y examinaremos los hallazgos de nuestro equipo en este blog.

Descripción General

La seguridad en blockchain se está volviendo cada vez más relevante en el ciberespacio actual y por eso es importante fortalecer la seguridad de cada capa de su arquitectura. Nuestro proyecto se centra en las capas más bajas del blockchain, en particular en la red P2P que permite que los nodos se comuniquen entre sí y compartan información. Esta capa es de vital importancia para cualquier blockchain, incluido Ethereum, debido a la naturaleza descentralizada de su arquitectura. No obstante, la capa de red P2P puede ser vulnerable a varios ataques, como una Denegación de Servicio Distribuida (DDoS), ataques de eclipse y ataques Sybil. En conclusión, esta capa es propensa a muchas amenazas heredadas de las redes P2P, y existe la necesidad de analizarlas y comprenderlas recopilando datos y extrayendo información del comportamiento de la red para reducir los riesgos asociados a estos ataques. Nosotros presentamos Tikuna, una herramienta de código abierto para monitorear y detectar posibles ataques a la red blockchain de Ethereum, en una etapa temprana del ataque. Tikuna usa un método de memoria a corto plazo no supervisado (LSTM por sus siglas en inglés), basado en redes neuronales recurrentes (RRN) para la detección de anomalías asociadas a posibles ataques en la red P2P. Los resultados empíricos obtenidos, indican que el algoritmo propuesto mejora significativamente el rendimiento de detección de ataques, con la capacidad de encontrarlos y clasificarlos, incluyendo ataques Eclipse, ataques Covert Flash y de otro tipo dirigidos a la capa P2P del blockchain Ethereum, con alta precisión.

Nuestro proyecto tenía como objetivo implementar Tikuna, una Prueba de Concepto, para un sistema de monitoreo de seguridad, del blockchain Ethereum. Con Tikuna nuestro objetivo era mejorar la visibilidad del estado de la red P2P. El proyecto se compone de tres entregables:

  1. Desarrollo de una solución de monitoreo P2P de código abierto accesible para la comunidad de EF.
  2. Creación de dashboards para visualizar diferentes aspectos de monitoreo de la red P2P.
  3. El borrador de un paper de investigación para presentar los hallazgos científicos de nuestro proyecto Tikuna.

Tikuna, el primer proyecto en América Latina en obtener fondos de investigación académica de la Fundación Ethereum, ha creado una estrategia novedosa para aumentar la conciencia sobre el estado de seguridad de la red P2P. Estamos orgullosos de contribuir al avance de la seguridad del blockchain y mejorar la seguridad del blockchain de Ethereum. Para obtener más información sobre Tikuna, visite nuestro sitio web [19].

Roadmap de la Fase I

Hallazgos significativos

Presentamos tres contribuciones principales:

  • Nuestro algoritmo propuesto de detección de anomalías de aprendizaje automático, puede detectar varios ataques en la capa P2P de Ethereum utilizando datos de seguimiento de eventos de nodos en un entorno de simulación.

  • Demostramos cómo detectar ataques de eclipse en un nodo en la red principal mediante la extracción de datos de registro de conexión generados de forma personalizada del cliente Ethereum Prysm y el uso de una red neuronal LSTM.

  • Desarrollamos un exploit personalizado que implementa un ataque de eclipse en el mundo real. Se probó con un cliente Prysm modificado en la red principal, por lo que no reenvía spam a la red. Con esto, podríamos probar la efectividad de nuestro algoritmo.

El paso inicial de nuestro equipo fue realizar una investigación y un análisis en profundidad para obtener una comprensión completa de los diversos algoritmos de seguridad P2P existentes, es decir, el estado del arte. En base a esto, desarrollamos un plan de acción integral que describe nuestro algoritmo y metodología en el proyecto Tikuna.

Posteriormente, procedimos a implementar un algoritmo basado en LSTM para el monitoreo de la seguridad y la detección de anomalías de la red blockchain de Ethereum. Además, nuestro equipo desarrolló e implementó tres dashboards destinados a mejorar la seguridad de la red P2P. Estos dashboards son: el de estado de la red P2P de Ethereum, el dashboard para el monitoreo de nodos Beacon y el dashboard de ataques de Eclipse, los cuales describiremos más adelante.

Finalmente, a lo largo del proceso de desarrollo, probamos rigurosamente nuestro algoritmo para asegurarnos de que fuera funcional. También llevamos a cabo varios experimentos en dos entornos de red distintos: el ambiente de pruebas simulado y la red principal de Ethereum, para evaluar minuciosamente la eficacia y el rendimiento del algoritmo de Tikuna.

La siguiente es una muestra de los datos de entrenamiento utilizados para Tikuna en la red principal de Ethereum, que consta de datos de conexión de descubrimiento (UDP) de nodos atacantes normales y nodos atacantes simulados. Los datos normales se recopilaron de varios nodos durante 3 días en funcionamiento normal, y los datos maliciosos se recopilaron de un solo nodo Ethereum víctima del atacado simulado, utilizando nuestro exploit. Cada línea tiene varias funciones de entrada, incluida la marca de tiempo, la IP y el puerto eliminados de la tabla de nodos, la IP y el puerto agregados a la tabla de nodos y el bucket donde se agrega el nodo.

El model y las simulaciones

Nosotros hemos utilizado RNN's para nuestra investigación. Son un tipo de modelo frecuentemente utilizado para procesar datos secuenciales como series de tiempo. Estos modelos están especializados en procesar una secuencia de valores en función del tiempo. Los RNN's pueden escalar a largas secuencias que no serían prácticas para redes sin especialización basada en secuencias. La mayoría de las redes recurrentes también pueden procesar secuencias de longitud variable.

Uno de estos modelos es de especial interés para nuestra investigación. El modelo de memoria a corto plazo largo (LSTM) utiliza un mecanismo de puerta para garantizar la propagación adecuada de la información a través de muchas iteraciones en el tiempo. Las redes LSTM tienen una celda de memoria específica y pueden capturar dependencias a largo plazo en datos secuenciales. LSTM son herramientas valiosas para problemas de modelado de lenguaje. Las redes LSTM son una versión de las redes neuronales recurrentes útiles para largas secuencias de datos interrelacionadas. LSTM fue elegido en esta investigación para la detección de anomalías para encontrar conexiones maliciosas en la comunicación con otros nodos de un cliente de Ethereum.

A continuación se muestra un ejemplo de la salida del modelo de ML para la red principal donde obtuvimos los mejores resultados detectando ataques de eclipse, con 1’000,000 de líneas de registro de conexión normales para entrenamiento y alrededor de 3,000 líneas de registros de ataques de eclipse para su evaluación:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

Nostros utilizamos métricas de medidas estándar para la detección de intrusiones con ML, como f1, recuperación, precisión y exactitud. Revise el repositorio de Github y esté atento a la publicación de nuestro borrador del paper de investigación si desea obtener más detalles.

Dashboards

Como parte de nuestra investigación, nuestro equipo consideró importante mostrar los resultados obtenidos, así como una posible implementación. Para lograr esto, creamos tres dashboards de Grafana que están integrados con Prometheus. Estos dashboards capturan métricas de monitoreo y muestran el comportamiento de la red P2P, el vecindario y el nodo Prysm en tiempo real.

  • Estado de la red P2P Ethereum: La información que se muestra en el dashboard del estado de la red P2P Ethereum proviene de nuestro nodo Prysm y se obtiene al monitorear el puerto 8080. Brinda datos sobre el vecindario de nuestro nodo y cómo este interactúa con los otros nodos del Blockchain. Este muestra la cantidad de pares conectados, el socket actual y el principal, los diversos tipos de bibliotecas P2P que están conectados a nuestro nodo junto con sus calificaciones, así como información sobre la tasa de participación y la vitalidad de la red P2P. Además de esto, brinda información sobre los nodos validadores, como su número total, el estado de sus cuentas y la cantidad promedio de ether en sus saldos.

Img. 1 El dashboard del estado de la red P2P Ethereum

  • Beacon Node Monitor: Este dashboard está relacionado a la infraestructura de nuestro nodo de consenso Prysm, muestra información como el uso de CPU, RAM, espacio en disco, consumo de ancho de banda y detalles sobre el comportamiento de los discos. Esta información se recuperó del clúster de Kubernetes que sirve como host para nuestro nodo de consenso, donde reside actualmente.

Img. 2 El dashboard de monitoreo del nodo Beacon

  • Eclipse Attacks Dashboard: La creación del tablero final requirió el desarrollo de una interfaz que pudiera registrar los posibles ataques de eclipse descubiertos por nuestro algoritmo de aprendizaje automático y luego ingresar esos registros en el componente AlertManager de Prometheus. Después de eso, Grafana crea una tabla que muestra una fila para cada ataque de eclipse identificado en un nodo determinado.

Img. 3 El dashboard de ataques de eclipse

En Tikuna, creemos que hacer una red P2P más segura y confiable requiere accesibilidad y transparencia, por lo que hemos desarrollado una sección de usuario integral para nuestra prueba de concepto. Nuestra sección de usuario ofrece instrucciones detalladas y tutoriales que guían a los usuarios de todos los niveles técnicos a través del proceso de instalación y explican cómo interpretar la información presentada en los dashboards. Con Tikuna, una audiencia más amplia puede beneficiarse de nuestro sistema de monitoreo P2P. Estamos orgullosos de contribuir al avance de la seguridad de blockchain a través de nuestras ideas innovadoras y compromiso con soluciones fáciles de usar. Para obtener más información sobre Tikuna y cómo usar nuestro sistema de monitoreo, visite nuestro sitio web [19] y navegue a través de nuestra sección de usuario.

El borrador del paper de investigación

Actualmente estamos trabajando en un borrador de un paper de investigación que se basa en los hallazgos y resultados obtenidos durante nuestra investigación. Una vez que recibamos los comentarios de EF, tenemos la intención de enviar el paper de investigación a una conferencia internacional. The paper de investigación está organizado en cinco capítulos principales. +

Anunciando la Entrega de Nuestro Proyecto Tikuna en la Fase de Prueba de Concepto

· 11 min de lectura
Andres Gomez Ramirez
Francis Gomez Ramirez
Loui Al Sardy

¡Estamos encantados de anunciar la finalización de la primera fase de nuestro proyecto Tikuna! Después de meses de arduo trabajo, dedicación y colaboración, nos complace compartir los resultados de la primera ronda de la beca de investigación académica de la Fundación Ethereum (EF). Destacaremos los hitos de nuestro proyecto y examinaremos los hallazgos de nuestro equipo en este blog.

Descripción General

La seguridad en blockchain se está volviendo cada vez más relevante en el ciberespacio actual y por eso es importante fortalecer la seguridad de cada capa de su arquitectura. Nuestro proyecto se centra en las capas más bajas del blockchain, en particular en la red P2P que permite que los nodos se comuniquen entre sí y compartan información. Esta capa es de vital importancia para cualquier blockchain, incluido Ethereum, debido a la naturaleza descentralizada de su arquitectura. No obstante, la capa de red P2P puede ser vulnerable a varios ataques, como una Denegación de Servicio Distribuida (DDoS), ataques de eclipse y ataques Sybil. En conclusión, esta capa es propensa a muchas amenazas heredadas de las redes P2P, y existe la necesidad de analizarlas y comprenderlas recopilando datos y extrayendo información del comportamiento de la red para reducir los riesgos asociados a estos ataques. Nosotros presentamos Tikuna, una herramienta de código abierto para monitorear y detectar posibles ataques a la red blockchain de Ethereum, en una etapa temprana del ataque. Tikuna usa un método de memoria a corto plazo no supervisado (LSTM por sus siglas en inglés), basado en redes neuronales recurrentes (RRN) para la detección de anomalías asociadas a posibles ataques en la red P2P. Los resultados empíricos obtenidos, indican que el algoritmo propuesto mejora significativamente el rendimiento de detección de ataques, con la capacidad de encontrarlos y clasificarlos, incluyendo ataques Eclipse, ataques Covert Flash y de otro tipo dirigidos a la capa P2P del blockchain Ethereum, con alta precisión.

Nuestro proyecto tenía como objetivo implementar Tikuna, una Prueba de Concepto, para un sistema de monitoreo de seguridad, del blockchain Ethereum. Con Tikuna nuestro objetivo era mejorar la visibilidad del estado de la red P2P. El proyecto se compone de tres entregables:

  1. Desarrollo de una solución de monitoreo P2P de código abierto accesible para la comunidad de EF.
  2. Creación de dashboards para visualizar diferentes aspectos de monitoreo de la red P2P.
  3. El borrador de un paper de investigación para presentar los hallazgos científicos de nuestro proyecto Tikuna.

Tikuna, el primer proyecto en América Latina en obtener fondos de investigación académica de la Fundación Ethereum, ha creado una estrategia novedosa para aumentar la conciencia sobre el estado de seguridad de la red P2P. Estamos orgullosos de contribuir al avance de la seguridad del blockchain y mejorar la seguridad del blockchain de Ethereum. Para obtener más información sobre Tikuna, visite nuestro sitio web [19].

Roadmap de la Fase I

Hallazgos significativos

Presentamos tres contribuciones principales:

  • Nuestro algoritmo propuesto de detección de anomalías de aprendizaje automático, puede detectar varios ataques en la capa P2P de Ethereum utilizando datos de seguimiento de eventos de nodos en un entorno de simulación.

  • Demostramos cómo detectar ataques de eclipse en un nodo en la red principal mediante la extracción de datos de registro de conexión generados de forma personalizada del cliente Ethereum Prysm y el uso de una red neuronal LSTM.

  • Desarrollamos un exploit personalizado que implementa un ataque de eclipse en el mundo real. Se probó con un cliente Prysm modificado en la red principal, por lo que no reenvía spam a la red. Con esto, podríamos probar la efectividad de nuestro algoritmo.

El paso inicial de nuestro equipo fue realizar una investigación y un análisis en profundidad para obtener una comprensión completa de los diversos algoritmos de seguridad P2P existentes, es decir, el estado del arte. En base a esto, desarrollamos un plan de acción integral que describe nuestro algoritmo y metodología en el proyecto Tikuna.

Posteriormente, procedimos a implementar un algoritmo basado en LSTM para el monitoreo de la seguridad y la detección de anomalías de la red blockchain de Ethereum. Además, nuestro equipo desarrolló e implementó tres dashboards destinados a mejorar la seguridad de la red P2P. Estos dashboards son: el de estado de la red P2P de Ethereum, el dashboard para el monitoreo de nodos Beacon y el dashboard de ataques de Eclipse, los cuales describiremos más adelante.

Finalmente, a lo largo del proceso de desarrollo, probamos rigurosamente nuestro algoritmo para asegurarnos de que fuera funcional. También llevamos a cabo varios experimentos en dos entornos de red distintos: el ambiente de pruebas simulado y la red principal de Ethereum, para evaluar minuciosamente la eficacia y el rendimiento del algoritmo de Tikuna.

La siguiente es una muestra de los datos de entrenamiento utilizados para Tikuna en la red principal de Ethereum, que consta de datos de conexión de descubrimiento (UDP) de nodos atacantes normales y nodos atacantes simulados. Los datos normales se recopilaron de varios nodos durante 3 días en funcionamiento normal, y los datos maliciosos se recopilaron de un solo nodo Ethereum víctima del atacado simulado, utilizando nuestro exploit. Cada línea tiene varias funciones de entrada, incluida la marca de tiempo, la IP y el puerto eliminados de la tabla de nodos, la IP y el puerto agregados a la tabla de nodos y el bucket donde se agrega el nodo.

El model y las simulaciones

Nosotros hemos utilizado RNN's para nuestra investigación. Son un tipo de modelo frecuentemente utilizado para procesar datos secuenciales como series de tiempo. Estos modelos están especializados en procesar una secuencia de valores en función del tiempo. Los RNN's pueden escalar a largas secuencias que no serían prácticas para redes sin especialización basada en secuencias. La mayoría de las redes recurrentes también pueden procesar secuencias de longitud variable.

Uno de estos modelos es de especial interés para nuestra investigación. El modelo de memoria a corto plazo largo (LSTM) utiliza un mecanismo de puerta para garantizar la propagación adecuada de la información a través de muchas iteraciones en el tiempo. Las redes LSTM tienen una celda de memoria específica y pueden capturar dependencias a largo plazo en datos secuenciales. LSTM son herramientas valiosas para problemas de modelado de lenguaje. Las redes LSTM son una versión de las redes neuronales recurrentes útiles para largas secuencias de datos interrelacionadas. LSTM fue elegido en esta investigación para la detección de anomalías para encontrar conexiones maliciosas en la comunicación con otros nodos de un cliente de Ethereum.

A continuación se muestra un ejemplo de la salida del modelo de ML para la red principal donde obtuvimos los mejores resultados detectando ataques de eclipse, con 1’000,000 de líneas de registro de conexión normales para entrenamiento y alrededor de 3,000 líneas de registros de ataques de eclipse para su evaluación:

2023-03-06 18:52:07,641 P7 INFO Epoch 84/100, training loss: 1.98741
2023-03-06 18:52:07,661 P7 INFO Evaluating test data.
2023-03-06 18:52:18,910 P7 INFO Finish inference. Show iteration top-k results:
2023-03-06 18:52:19,272 P7 INFO {'f1': '0.847', 'Recall': '0.883', 'Precision': '0.813', 'Accuracy': '0.866'}
2023-03-06 18:52:19,284 P7 INFO {'f1': '0.804', 'Recall': '0.757', 'Precision': '0.858', 'Accuracy': '0.846'}
2023-03-06 18:52:19,296 P7 INFO {'f1': '0.740', 'Recall': '0.645', 'Precision': '0.868', 'Accuracy': '0.811'}
2023-03-06 18:52:19,308 P7 INFO {'f1': '0.704', 'Recall': '0.588', 'Precision': '0.879', 'Accuracy': '0.794'}
2023-03-06 18:52:19,320 P7 INFO {'f1': '0.683', 'Recall': '0.554', 'Precision': '0.889', 'Accuracy': '0.785'}

Nostros utilizamos métricas de medidas estándar para la detección de intrusiones con ML, como f1, recuperación, precisión y exactitud. Revise el repositorio de Github y esté atento a la publicación de nuestro borrador del paper de investigación si desea obtener más detalles.

Dashboards

Como parte de nuestra investigación, nuestro equipo consideró importante mostrar los resultados obtenidos, así como una posible implementación. Para lograr esto, creamos tres dashboards de Grafana que están integrados con Prometheus. Estos dashboards capturan métricas de monitoreo y muestran el comportamiento de la red P2P, el vecindario y el nodo Prysm en tiempo real.

  • Estado de la red P2P Ethereum: La información que se muestra en el dashboard del estado de la red P2P Ethereum proviene de nuestro nodo Prysm y se obtiene al monitorear el puerto 8080. Brinda datos sobre el vecindario de nuestro nodo y cómo este interactúa con los otros nodos del Blockchain. Este muestra la cantidad de pares conectados, el socket actual y el principal, los diversos tipos de bibliotecas P2P que están conectados a nuestro nodo junto con sus calificaciones, así como información sobre la tasa de participación y la vitalidad de la red P2P. Además de esto, brinda información sobre los nodos validadores, como su número total, el estado de sus cuentas y la cantidad promedio de ether en sus saldos.

Img. 1 El dashboard del estado de la red P2P Ethereum

  • Beacon Node Monitor: Este dashboard está relacionado a la infraestructura de nuestro nodo de consenso Prysm, muestra información como el uso de CPU, RAM, espacio en disco, consumo de ancho de banda y detalles sobre el comportamiento de los discos. Esta información se recuperó del clúster de Kubernetes que sirve como host para nuestro nodo de consenso, donde reside actualmente.

Img. 2 El dashboard de monitoreo del nodo Beacon

  • Eclipse Attacks Dashboard: La creación del tablero final requirió el desarrollo de una interfaz que pudiera registrar los posibles ataques de eclipse descubiertos por nuestro algoritmo de aprendizaje automático y luego ingresar esos registros en el componente AlertManager de Prometheus. Después de eso, Grafana crea una tabla que muestra una fila para cada ataque de eclipse identificado en un nodo determinado.

Img. 3 El dashboard de ataques de eclipse

En Tikuna, creemos que hacer una red P2P más segura y confiable requiere accesibilidad y transparencia, por lo que hemos desarrollado una sección de usuario integral para nuestra prueba de concepto. Nuestra sección de usuario ofrece instrucciones detalladas y tutoriales que guían a los usuarios de todos los niveles técnicos a través del proceso de instalación y explican cómo interpretar la información presentada en los dashboards. Con Tikuna, una audiencia más amplia puede beneficiarse de nuestro sistema de monitoreo P2P. Estamos orgullosos de contribuir al avance de la seguridad de blockchain a través de nuestras ideas innovadoras y compromiso con soluciones fáciles de usar. Para obtener más información sobre Tikuna y cómo usar nuestro sistema de monitoreo, visite nuestro sitio web [19] y navegue a través de nuestra sección de usuario.

El borrador del paper de investigación

Actualmente estamos trabajando en un borrador de un paper de investigación que se basa en los hallazgos y resultados obtenidos durante nuestra investigación. Una vez que recibamos los comentarios de EF, tenemos la intención de enviar el paper de investigación a una conferencia internacional. The paper de investigación está organizado en cinco capítulos principales. El primer capítulo es una introducción a la cadena de bloques de Ethereum, que enfatiza la importancia de comprender los riesgos asociados con las redes P2P de Blockchain y desarrollar soluciones centradas en la seguridad para garantizar su confiabilidad. También destaca las tres contribuciones principales del proyecto, que incluyen el algoritmo de aprendizaje automático propuesto para detectar ataques en la capa P2P, un método para detectar ataques de eclipse utilizando datos de registro de conexión generados de forma personalizada y redes neuronales LSTM, y un exploit personalizado para ejecutar ataques de eclipse. El segundo capítulo, titulado "Trabajos relacionados", ofrece una descripción general de los trabajos más recientes que abordan los desafíos de seguridad de las redes P2P de blockchain de Ethereum. El tercer capítulo presenta el algoritmo de Tikuna describiendo cada uno de los tres pasos y discutiendo los diversos tipos de ataques a la red P2P de blockchain. El cuarto capítulo evalúa la efectividad del algoritmo de Tikuna utilizando un conjunto de datos de simulación y de conexión a la red principal. El último capítulo presenta un resumen del trabajo propuesto, las conclusiones y las posibles direcciones futuras de investigación. Finalmente, los autores reconocen que el trabajo presentado fue apoyado por las becas de investigación del EF.

Siguientes pasos

Nos enorgullecemos enormemente de los logros de nuestro equipo y extendemos nuestra gratitud a todos los que contribuyeron a garantizar el éxito del proyecto. La experiencia, la dedicación y el espíritu colaborativo de nuestro equipo fueron cruciales para entregar la primera fase del proyecto Tikuna. Este es solo el comienzo de nuestro proyecto, ya que nuestro objetivo es desarrollar Tikuna como una herramienta para contribuir a la comunidad Ethereum y otras Blockchains. Nuestros esfuerzos se centrarán en identificar ataques adicionales, minimizar los falsos positivos, detectar incidentes del mundo real e incorporar diferentes clientes de nodos de Blockchain (incluido Ethereum). Además, tenemos la intención de investigar otras áreas donde se puede utilizar Tikuna, como el valor máximo extraíble (MEV).

Hemos solicitado la segunda ronda de las becas de investigación académicas del EF. Consulte nuestra aplicación aquí. Si está interesado en aprender más sobre Tikuna o trabajar con nosotros, agradecemos la oportunidad de colaborar con usted. Comuníquese con nosotros y estaremos encantados de saber de usted.



- - +
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Acerca Sakundi


Sakundi es una organización de seguridad y privacidad de Blockchain basada en la investigación. Ofrece soluciones de monitoreo de seguridad para organizaciones Blockchain y aplicaciones distribuidas basadas en herramientas de Inteligencia Artificial.

+ + \ No newline at end of file diff --git a/es/docs/about/team/index.html b/es/docs/about/team/index.html index 99e3e9e..9c8fbc7 100644 --- a/es/docs/about/team/index.html +++ b/es/docs/about/team/index.html @@ -3,23 +3,19 @@ -El equipo de Tikuna | Blockchain Security Monitoring - - - - - - - +El equipo de Tikuna | Blockchain Security Monitoring + + +
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El equipo de Tikuna


Conoce al grupo de profesionales detrás de Tikuna.

Andrés Gómez Ramírez

Andrés Gómez Ramírez

Investigador de seguridad líder

Lider del proyecto Tikuna. Doctor en ciberseguridad de la Universidad de Frankfurt y el CERN. Investigador de seguridad de blockchain en Edenia y founder en Sakundi.

Francis Gómez Ramírez

Francis Gómez Ramírez

Ingeniero sénior de DevOps

Informático y especialista en gestión de proyectos con amplia experiencia en la industria bancaria y fintech. Co-founder en Sakundi.

Loui Al Sardy

Loui Al Sardy

Investigador de seguridad

Doctor. Candidato en el Departamento de Ingeniería de Software en la Universidad de Erlangen-Nuremberg. Co-founder en Sakundi

Xavier Fernández

Xavier Fernández

Gerente de proyecto

Socio de Edenia enfocado en infraestructura blockchain y desarrollo de soluciones web3. Xavier ha trabajado en una amplia gama de proyectos, desde código abierto impulsado por la comunidad hasta desarrollo empresarial utilizando diversas implementaciones de tecnología blockchain.

Edgar Fernández

Edgar Fernández

Líder de Negocios

Nuestra empresa nació con internet. Ahora nuestro objetivo es transformarlo.

Edwin Rodríguez

Edwin Rodríguez

UI / UX y diseñador gráfico

Me apasiona lo que hago y disfruto estar rodeado de personas que me inspiran y me empujan a mejorar mis habilidades.

Paola Espinoza

Paola Espinoza

Desarrollador web

Desarrolladora web motivada que busca mejorar en Edenia. Apasionada por la construcción de aplicaciones web y soluciones blockchain.

Johanna Yee

Johanna Yee

Diseñador gráfico

Considero que la creatividad y la innovación son indispensables para proponer soluciones eficientes y disruptivas.

Leo Calderon

Leo Calderon

Asociado de negocios

Aportando desde la estrategia para conectar proyectos web3 a oportunidades y personas.

+ + \ No newline at end of file diff --git a/es/docs/monitoring/beacon-node/index.html b/es/docs/monitoring/beacon-node/index.html index 2d66e65..f0ec824 100644 --- a/es/docs/monitoring/beacon-node/index.html +++ b/es/docs/monitoring/beacon-node/index.html @@ -3,23 +3,19 @@ -Monitoreo del Nodo Beacon | Blockchain Security Monitoring - - - - - - - +Monitoreo del Nodo Beacon | Blockchain Security Monitoring + + + + + \ No newline at end of file diff --git a/es/docs/monitoring/eclipse-attacks/index.html b/es/docs/monitoring/eclipse-attacks/index.html index dc1e94c..fc7c55d 100644 --- a/es/docs/monitoring/eclipse-attacks/index.html +++ b/es/docs/monitoring/eclipse-attacks/index.html @@ -3,23 +3,19 @@ -Ataques Eclipse | Blockchain Security Monitoring - - - - - - - +Ataques Eclipse | Blockchain Security Monitoring + + + + + \ No newline at end of file diff --git a/es/docs/monitoring/network-eth2/index.html b/es/docs/monitoring/network-eth2/index.html index 4d7e571..d26a634 100644 --- a/es/docs/monitoring/network-eth2/index.html +++ b/es/docs/monitoring/network-eth2/index.html @@ -3,23 +3,19 @@ -Estado de la red P2P ETH2 | Blockchain Security Monitoring - - - - - - - +Estado de la red P2P ETH2 | Blockchain Security Monitoring + + + + + \ No newline at end of file diff --git a/es/docs/research/glossary/index.html b/es/docs/research/glossary/index.html index e58a1cc..2ad48b8 100644 --- a/es/docs/research/glossary/index.html +++ b/es/docs/research/glossary/index.html @@ -3,23 +3,19 @@ -Glosario | Blockchain Security Monitoring - - - - - - - +Glosario | Blockchain Security Monitoring + + +
-

Glosario

Lista de términos necesarios para comprender un poco mas los contenidos presentes en este trabajo.

A

API (Interfaz de Programación de Aplicaciones)

Conjunto de funciones y procedimientos que permiten la creación de aplicaciones que acceden a las características o datos de un sistema operativo, aplicación u otro servicio especifico.


B

Backend

Backend es la capa de acceso a datos de un software, que no es directamente accesible por los usuarios,contiene la lógica de la aplicación que maneja dichos datos. El Backend accede al servidor, que es una aplicación especializada que entiende la forma como el software cliente solicita información.


Billeteras

Las billeteras son clientes que almacenan llaves privadas asociadas con los permisos de una o más cuentas. Idealmente, una billetera tiene un estado bloqueado (cifrado) y desbloqueado (sin cifrar) que está protegido por una contraseña de alta entropía.


Blacklist (Lista negra)

Una lista de nodos o cuentas a las cual un peer no desea conectarse puede definirse como una lista de cuentas bloqueadas. Cada nodo puede definir su propia lista negra, que no se puede ver públicamente.


Blockchain

Es una lista creciente de registros, llamados bloques, que están vinculados mediante criptografía. Cada bloque contiene un hash criptográfico del bloque anterior, una marca de tiempo y datos de transacciones (generalmente representados como un árbol Merkle).


Block Explorer (Explorador de Bloques)

Un explorador de bloques es un programa o sitio web accesible a través de un navegador que permite a un usuario buscar y navegar por los bloques de una cadena de bloques, sus contenidos y sus detalles relevantes.


Block Height (Altura del Bloque)

Se define como el número de bloques en la cadena entre un bloque dado y el primer bloque en la cadena de bloques. Por ejemplo una altura de bloque de 500 se refiere al bloque número 500 de esa cadena.


Bloque

Los bloques son conjuntos de registros donde los datos pertenecientes a la cadena de bloques se registran de forma permanente. Almacenan algunas o todas las transacciones más recientes que aún no han ingresado a ningún bloque anterior. Un bloque es un almacén permanente de registros que una vez escritos no pueden ser alterados o eliminados.


Bytecode Canónico

Es utilizado para la comunicación entre diferentes formatos de datos. Una forma de integración de aplicaciones empresariales, pretende reducir los costos y estandarizar las definiciones acordadas de datos asociados con la integración de los sistemas de negocio.


C

Cuenta de contrato

Una cuenta que contiene código que se ejecuta cada vez que recibe una transacción de otra cuenta (EOA o contrato).


Cuenta

Un objeto que tiene una dirección, un saldo, un nonce y posiblemente también algo de almacenamiento y código. Una cuenta puede ser una cuenta de contrato o una cuenta de propiedad externa (EOA).


D

DApps

Son aplicaciones o programas digitales se ejecutan en una red de computadoras blockchain o P2P en lugar de una sola computadora, estas están fuera del alcance y control de una sola autoridad.


DevOps

Es un conjunto de prácticas que combina el desarrollo de software y las operaciones de TI. Su objetivo es acortar el ciclo de vida del desarrollo de sistemas y proporcionar una entrega continua con alta calidad de software.


Descubrimiento

El método por el cual un nodo Ethereum identifica otros nodos para conectarse.


Docker

Es un conjunto de productos de plataforma como servicio que utiliza la virtualización a nivel del sistema operativo para entregar software en paquetes llamados contenedores. Los contenedores están aislados unos de otros y agrupan su propio software, bibliotecas y archivos de configuración; pueden comunicarse entre sí a través de canales bien definidos. Todos los contenedores son ejecutados por un solo núcleo del sistema operativo, por lo tanto, usan menos recursos que las máquinas virtuales.


Dirección cero

Una dirección especial de Ethereum, compuesta completamente de ceros, que se especifica como la dirección de destino de una transacción de creación de contrato.


Dirección

Por lo general, se trata de un EOA o contrato que puede recibir (dirección de destino) o enviar (dirección de origen) transacciones de cadena de bloques. Además, son los 160 bits más a la derecha del hash Keccak de una clave pública ECDSA.


E

Encabezado del Bloque

El encabezado del bloque es una colección de metadatos específicos del bloque y un resumen de las transacciones contenidas en la carga de ejecución.


Ether

La criptomoneda nativa que utiliza el ecosistema Ethereum y que se utiliza para pagar las tarifas de gas cuando se ejecutan las transacciones.


Estado

Una copia instantánea de todos los saldos y datos en un momento determinado en la cadena de bloques, que generalmente se refiere a la condición en un bloque específico.


F

Fiat

El dinero fiduciario (fiat) es una moneda emitida por el gobierno que no está respaldada por un producto físico, como el oro o la plata, sino por el gobierno que la emitió.


Fork

Un cambio en el protocolo que provoca la creación de una cadena alternativa o una divergencia temporal en dos posibles rutas de bloqueo durante la minería.


G

Genesis JSON

genesis.json es el archivo que define el Genesis block, el cual es el bloque inicial de la cadena de bloques. Este es como la "configuración" de su blockchain. Por ejemplo, la configuración de la cadena y el nivel de dificultad para extraer bloques.


GitHub

Es un plataforma que proporciona alojamiento para el control de versiones de desarrollo de software, ademas, permite manejar control de versiones distribuidas y la funcionalidad de gestión de código fuente, así como de sus propias características. Proporciona control de acceso y varias funciones de colaboración, como seguimiento de errores , solicitudes de funciones, gestión de tareas y wikis para cada proyecto.


Gas

Un combustible virtual utilizado en Ethereum para ejecutar contratos inteligentes. La EVM utiliza un mecanismo contable para medir el consumo de gas y limitar el consumo de recursos informáticos.


Gas Limite

La cantidad máxima de gas que puede consumir una transacción o bloque.


H

Head Block

El Head Block es el último bloque escrito en la cadena de bloques, almacenado en bloques reversibles.


I

IAC (Infraestructura como Código)

Es el proceso de gestión de centros de datos informáticos a través de archivos de definición legibles por máquina, en lugar de configuración de hardware físico o herramientas de configuración interactivas.


IBC (Comunicación Inter-Blockchain)

Es una herramienta por la cual múltiples blockchains pueden comunicarse entre sí. Esta puede usarse para enviar tokens de una cadena a otra, o para expandir la capacidad de una red al aumentar el número de cadenas que se ejecutan en paralelo.


Immutable

Término utilizado para referirse a algo que no se puede cambiar.


IPFS (Almacenamiento Descentralizado)

Es un mecanismo de almacenamiento distribuido para colocar archivos en línea para almacenamiento descentralizado.


Issue

Los issues son una excelente manera de realizar un seguimiento de las tareas, mejoras y errores para sus proyectos a través de la plataforma de GitHub.


J

K

Kubernetes

Kubernetes es una plataforma portátil, extensible y de código abierto para gestionar cargas de trabajo y servicios en contenedores, que facilita tanto la configuración declarativa como la automatización. Tiene un ecosistema grande y de rápido crecimiento. Los servicios, el soporte y las herramientas de Kubernetes están ampliamente disponibles.


L

Latencia

Periodo de tiempo que transcurre entre la comunicación de un paquete atraves de la red.


Launch

Término utilizado para referirse a el comienzo de una cadena de bloques.


Llaves Privadas (también conocido como clave secreta)

Una llave privada es una clave secreta utilizada para firmar transacciones. En EOSIO, la autoridad de una clave privada está determinada por su asignación a un nombre de cuenta EOSIO.


La red

La red Ethereum es una red peer-to-peer que transmite transacciones y bloques a cada nodo Ethereum (participante de la red).


M

Merkle Tree

Un Merkle Tree es un árbol en el que cada nodo de hoja se etiqueta con el hash de un bloque de datos, y cada nodo que no es de hoja se etiqueta con el hash criptográfico de las etiquetas de sus nodos secundarios.

Los árboles de hash permiten la verificación eficiente y segura del contenido de grandes estructuras de datos.


Multi-Sig

Multisig es un corto plazo para múltiples firmas. Se utiliza para describir el caso en el que se requiere el permiso de más de una cuenta para ejecutar una transacción.


Mensaje

Una transacción interna que nunca se serializa y solo se envía dentro de la EVM.


Monedero no alojado

Un tipo de billetera de autocustodia que permite a los usuarios mantener sus saldos de criptomonedas independientemente de los intercambios o de terceros.


Moneda de utilidad

Un token que generalmente brinda a los titulares la capacidad de acceder, a través de una clave cifrada, a una cadena de bloques o red en particular con el fin de acceder a ciertos beneficios o funcionalidades en esa cadena de bloques o plataforma. Los tokens de utilidad generalmente no están destinados para su uso como moneda o medio de pago.


N

Nodos

Un nodo es la base fundamental de la tecnología blockchain, conforman la infraestructura de una cadena de bloques. Todos los nodos en una cadena de bloques están conectados entre sí e intercambian constantemente los últimos datos de la cadena de bloques para que todos los nodos se mantengan actualizados.


O

Oracle

Es un agente que encuentra y verifica las ocurrencias del mundo real y envía esta información a una cadena de bloques para que la usen los contratos inteligentes.


P

Pull Request

Los pull request permiten informar a otros usuarios sobre los cambios que ha realizado en una rama de un repositorio en GitHub.

Una vez que se abre un pull request, se puede analizar y revisar los posibles cambios con los colaboradores y agregar commits de seguimiento antes de que sus cambios se fusionen en la rama base.


Proof of Work

Los nodos mineros compiten para encontrar un nonce agregado al encabezado de un bloque que hace que el bloque tenga alguna propiedad deseada (típicamente un cierto número de ceros en los bits más significativos del hash criptográfico del encabezado del bloque). Al hacer que sea computacionalmente costoso encontrar tales nonces que hacen que los bloques sean válidos, se hace difícil para los atacantes crear una bifurcación alternativa de la cadena de bloques que el resto de la red aceptaría como la mejor cadena.


Par

Computadoras que ejecutan software de cliente Ethereum que tienen copias idénticas de la cadena de bloques y están conectadas entre sí.


Papel blanco

Un documento publicado por los desarrolladores que explican la tecnología y el propósito del proyecto en el que están trabajando. El documento les dice a los posibles inversores cómo se concibió la criptomoneda y destaca su propósito.


Q

R

Recibo

Datos devueltos por un cliente de Ethereum para representar el resultado de una transacción en particular, incluido un hash de la transacción, su número de bloque, la cantidad de gas utilizada y, en caso de implementación de un contrato inteligente, la dirección del contrato.


Prefijo de longitud recursiva (RLP)

Prefijo de longitud recursivo. Un estándar de codificación diseñado por los desarrolladores de Ethereum para codificar y serializar objetos (estructuras de datos) de complejidad y longitud arbitrarias.

S

Serialización

El proceso de convertir una estructura de datos en una secuencia de bytes.


Stake

El Staking es el acto de bloquear tokens para recursos en una red EOSIO. Esto incluye, pero no se limita a, tiempo de CPU, RAM y gobierno en cadena.


Solidity

Un lenguaje de programación procedimental que tiene una sintaxis comparable a JavaScript, C++ y Java. El lenguaje más utilizado y popular para los contratos inteligentes de Ethereum.

Sincronizando

El proceso de descargar la última versión completa de una cadena de bloques en un nodo.


T

TestNet

Una red de prueba o testnet es una instancia de la plataforma EOSIO destinada a fines de prueba. En general, el token nativo no tiene valor y se entrega a los desarrolladores para que puedan probarlo.


Tokens

Es la unidad de medida de una criptomoneda.


Transacciones

Es una combinación de una o más acciones. Por lo general, el resultado de la ejecución de un contrato inteligente.


TPS (Transacciones por Segundo)

Una métrica mediante la cual se mide el rendimiento de una cadena de bloques.


Tarifa base

El precio de reserva de cada bloque se conoce como "tarifa base". Es la tarifa mínima de gas que debe pagar un usuario para que una transacción sea incluida en el bloque posterior.


Tabla hash distribuida (DHT)

Una estructura de datos que comprende pares (clave, valor) que los nodos de Ethereum utilizan para identificar pares con los que conectarse y elegir qué protocolos utilizar para la comunicación.


U

V

Validador

Un nodo es responsable de almacenar datos, ejecutar transacciones y agregar nuevos bloques a la cadena de bloques en un sistema de prueba de participación. Para que el software de validación se active, se deben depositar en la cadena 32 ETH.


Validación de bloque

Verificar que un nuevo bloque tiene transacciones y firmas válidas, se basa en la cadena histórica más pesada y se adhiere a todos los demás requisitos de consenso. Los bloques válidos se agregan al final de la cadena y se distribuyen a la red, donde se descartan los bloques no válidos.


W

Wallet

Las Wallets son clientes que almacenan llaves privadas asociadas con los permisos de una o más cuentas. Idealmente, una billetera tiene un estado bloqueado (cifrado) y desbloqueado (sin cifrar) que está protegido por una contraseña de alta entropía.


Whitelist

Una lista blanca es una medida subjetiva que un nodo de blockchain puede poner en práctica para permitir que las transacciones se coloquen en un bloque solo si se enumeran en su cuenta o lista blanca de contrato. Cada nodo puede especificar su propia lista blanca, que no se puede ver públicamente.


Whisper

Un servicio de mensajería descentralizado (P2P). Se usa junto con Web3 y Swarm para construir DApps.


X

Y

Z

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Bienvenidos a Tikuna


Tikuna, una prueba de concepto de un sistema de monitoreo de seguridad de red P2P para la cadena de bloques de Ethereum. Aprovechando las técnicas de Machine Learning, extraerá información de seguridad y rendimiento para la detección temprana de incidentes relevantes. Tikuna brindará una mejor visibilidad del estado de seguridad de la red P2P.

Acerca del proyecto

Investigaremos cómo aplicar algoritmos de Machine Learning para monitorear la seguridad de la red P2P de Ethereum para la detección temprana de algunos ataques específicos. Desarrollaremos Tikuna, una prueba de concepto (código abierto para que todos los usuarios puedan usarla) derivada de dicha investigación, y proporcionaremos un informe científico basado en los hallazgos de implementar dicha herramienta en la red principal de Ethereum. También proporcionaremos un tablero simple para que la comunidad observe los hallazgos que proporcionará Tikuna. Además, crearemos un draft de un artículo científico basado en los resultados de la investigación.

Propósito del proyecto

Tikuna ayudará a la comunidad de Ethereum a obtener visibilidad sobre el estado de la red P2P y estar al tanto de posibles ataques.

El objetivo general del proyecto es apoyar a la comunidad Ethereum brindándoles una herramienta inteligente de última generación capaz de recopilar información relacionada con el estado de seguridad de la red P2P y mejorar la visibilidad de la red proporcionando información sobre su estado actual. Dicha información y datos recopilados pueden ayudar a la comunidad a medir el éxito de nuestro enfoque por la cantidad de incidentes identificados y el número de hallazgos descubiertos. Por lo tanto, nuestra herramienta desempeñará un papel importante en la mejora de la seguridad general de la red Ethereum.

Hoja de ruta del proyecto

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Referencias del proyecto


- - +
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Estado del Arte


Ethereum fue presentado formalmente por Vitalik Buterin en su whitepaper en 2014 [2] y lanzando en 2015; se considera una de las plataformas de blockchain públicas de criptomonedas de código abierto más conocidas que admite la funcionalidad de contratos inteligentes con Ether (ETH o Ξ) somo su criptomoneda nativa y Solidity como su lenguaje de programación. De acuerdo con [1], hubo un total de 105.58 millones de transacciones y 1.45 millones de contratos inteligentes creados en el primer cuarto (Q1) de 2022. Adicionalmente, sobre la capitalización de mercado, Ethereum es la segunda criptomoneda más grande después de Bitcoin, con al rededor de $234 billones en Agosto de 2022 [3].

Por otro lado, Ethereum permite a los desarrolladores crear aplicaciones descentralizadas (dApps) en diversos sectores, como las finanzas descentralizadas (DeFi) [4], juegos [5,6], tokens no fungibles (NFTs) [7], e Internet de las Cosas (IoT) [10] que se ejecutan en una red peer-to-peer (P2P), minimizando las posibilidades de fraude, censura, tiempo de inactividad o participación de terceros y haciéndolos más confiables. Además, la red principal pública de Ethereum acaba de pasar por una actualización significativa a Ethereum 2 (oficialmente después de cambiar el nombre llamado The Merge ($ETH) [8, 9]) para mejorar su rendimiento, eficiencia y escalabilidad.

Las blockchains tienen muchas capas, como la capa de consenso [11-13], capa de scripting, capa de red, o capa de DApps, pero la que nos interesa es la capa de nivel más bajo, es decir, la red P2P que permite que los nodos se interconecten entre sí y compartan información, la cual es un componente de vital importancia para la blockchain Ethereum. La idea básica detrás de la red P2P es que un grupo de computadoras llamadas nodos se conectan para compartir datos sin necesidad de una computadora centralizada, es decir, todos los nodos de la red tienen las mismas capacidades sin tener ningún privilegio especial, como el que se usa en los sistemas de intercambio de archivos Napster, LimeWire y Gnutella.

La red peer-to-peer (P2P) es el componente fundamental que sirve como base para cualquier tecnología blockchain. Pero, al igual que otras formas de tecnología, las redes P2P pueden verse afectadas por una serie de problemas de seguridad. Los atacantes pueden explotar algunas de las vulnerabilidades de la red P2P, para llevar a cabo varios vectores de ataque en la blockchain, como el ataque eclipse, ataque de mineria selfish, ataques Sybil, o ataques DDoS. Por esta razón, para abordar tanto los diferentes vectores de ataque en la plataforma Ethereum como las vulnerabilidades de seguridad de la red P2P, muchos investigadores han comenzado recientemente a enfocar sus esfuerzos en esta dirección.

A continuación se presentan algunos de los trabajos más recientes que abordan los desafíos de seguridad de las redes P2P de blockchain de Ethereum:

  • Este paper [14] se enfoca generalmente en los problemas de seguridad de cada capa en la blockchain de Ethereum, como la capa de red, proporcionando un análisis en profundidad que cubre las siguientes tres áreas:

    1. Los potenciales ataques como el ataque eclipse y el ataque account hijacking.
    2. Las vulnerabilidades que conducen a estos, por ejemplo, la vulnerabilidad de creación de nodos ilimitados y la vulnerabilidad de conexiones entrantes sin límite.
    3. Los efectos de cada incidente, como el doble gasto o la denegación de servicio (DoS).

    Además de ofrecer una visión general de la eficacia y las limitaciones de los Sistemas de Detección de Intrusos (IDS) existentes como técnica de defensa frente a estos ataques.

  • Este paper [15] propone el protocolo GossipSub y sus dos componentes, la construcción de la malla y la función de puntuación. El GossipSub es un protocolo de mensajería que permite la transmisión de mensajes de manera rápida y robusta en redes permisivas, como la capa de red de Ethereum 2.0, para hacerlo, por un lado, más seguro contra varios tipos de ataques (como el ataque Sybil, el ataque Eclipse y el ataque Cold Boot), y por otro lado, para facilitar una transmisión más rápida de los mensajes dentro del la red. Además, los autores describen algunas de las contramedidas que se presentan en el protocolo GossipSub, como el mantenimiento controlado de la malla, el grafting aportunista, y distribución adaptativa de los mensajes gossip. En conclusión, los escritores evalúan el protocolo GossipSub contra varios ataques y demuestran su resistencia a estos ataques.

  • En este estudio [16], los autores primero destacan parte de la estructura de la red Eth2, el ecosistema de la red y los peligros potenciales que podría plantear. Además, los escritores investigan exhaustivamente la red P2P de la red principal Eth2 mediante el desarrollo de una herramienta de monitoreo llamada Armiarma. Esta herramienta consta de dos componentes, el rastreador Armiarma y el analizador Armiarma, para abordar la falta de información sobre el rendimiento del protocolo GossipSub y sus nodos en la red principal Eth2. Adicional, se realizó un experimento para evaluar las capacidades del enfoque propuesto y se agrupó un análisis de los resultados obtenidos en las siguientes tres partes:

    1. Análisis de red Eth2.
    2. Interacción de clientes Eth2.
    3. Análisis de peers individuales

    Además, el análisis identifica varios problemas con la cadena Beacon de Eth2, incluido el alojamiento fisico geográfico de los validadores de Eth2. Finalmente, los autores concluyen que la red se está comportando de manera saludable; sin embargo, aún deben considerarse algunos temas relacionados con la descentralización.

  • Esta investigación [17] analiza "los ataques eclipse" en la red P2P de Ethereum. Un ataque eclipse es un ataque que permite a un atacante aislar un nodo dentro de la red P2P, al obtener el control completo del acceso a la información de un nodo o el control sobre todo lo que ve el nodo. Este ataque se analiza en el contexto de la red P2P de Ethereum. Los autores desarrollaron un modelo de detección de ataques eclipse llamado ETH-EDS que se dirige principalmente a la plataforma Ethereum. Este modelo utilizó la técnica de clasificación de bosques aleatorios para examinar los paquetes de datos normales y de ataque en la red. Los paquetes de datos recopilados incluían detalles como el tamaño de las etiquetas de los paquetes, la frecuencia con la que se accedía y el tiempo de acceso. Los hallazgos de los experimentos muestran que los nodos de red maliciosos podrían identificarse con un alto grado de precisión.

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Cómo usar Tikuna


Antes de empezar

  • Instalar docker https://docs.docker.com/engine/install/. Si ya tiene previamente instalado docker, verifiquelo usando el comando docker --version y continue con el siguiente paso.
  • Crea un directorio local llamado /data; si este directorio ya existe localmente, continue con el siguiente paso.
  • Copie los directorios que contienen la información al directorio /data creado en el paso anterior.

Instalación

Para correr este proyecto, instalelo localmente usando:

- $ git clone https://github.com/sakundi/tikuna
- $ cd tikuna
- $ docker build -t tikuna:latest .

Inicio rápido

Para empezar a usar Tikuna, ejecute los pasos descrito a continuación.

  • Dentro del directorio del proyecto Tikuna, vaya a la carpeta bin y ejecute el siguiente comando para iniciar un contenedor de docker:

       $ ./start_tikuna.sh
  • Dentro del contenedor de docker ejecute el siguiente comando para lanzar una sesión de Jupyter Notebook en donde los algoritmos de machine learning pueden ser ejecutados usando la data de Ethereum:

       $ ./bin/start_jupyter_notebook.sh
  • Después de ejecutar el comando anterior, usted solo necesita abrir su navegador, y luego copie y pegue la url mostrada en pantalla.

  • En el navegador, vaya hasta el directorio analysis, y luego haga click sobre él.
  • Click sobre el archivo ethereum_lstm_log.ipynb.
  • Por cada paso, haga click sobre el botón Run y espere hasta que los resultados empiecen a ser mostrados en pantalla.
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Entendiendo la data detrás de los Dashboards


P2P Network status on ETH2:

Este dashboard muestra toda la información importante almacenada historicamente dentro del nodo de consenso. Esta dividido en dos categorías: Neighborhood y Network.

  • El "neighborhood status" hace referencia a como el nodo de consenso está interactuando con otros nodos dentro de la red de blockchain. Se pueden encontrar estadísticas importantes como:

Peer Count:

Con cuantos nodos de la red, esta interactuando nuestro nodo beacon.

Max inactivity:

Es una puntuación que refleja la inactividad actual de los validadores.

Libp2p Peers pie chart:

Muestra cómo se distribuyen los agentes entre los pares vecinos, qué tipo de cliente Libp2p están usando y cuál es el más usado.

Libp2p Peers average scores:

La puntuación media obtenida en cuanto a las métricas y el rendimiento.

  • Por otro lado, el estado de la red muestra datos resumidos, medidos por el propio nodo, sobre los aspectos generales en el estado de la red tales como:

Participation rate:

Cuántos saldos de ETH se están certificando (validando) en la red.

Network Liveness:

This metric expresses the distance between the current finalized epoch and the expected finalized epoch based on the genesis time.

Epochs:

Esta métrica expresa la distancia entre el epoch finalizado actual y el epoch finalizado esperado en función del tiempo del epoch génesis.

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-

Un sistema de monitoreo de seguridad Blockchain basado en IA.

Así es como funciona:

Investigación

Echa un vistazo a los propósitos principales detrás del proyecto.

Sobre Tikuna

Conozca a nuestro equipo y empresas involucradas

Blog

Ve y lee los contenidos escritos por nuestro equipo

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-

Markdown page example

You don't need React to write simple standalone pages.

+ + \ No newline at end of file diff --git a/es/opensearch.xml b/es/opensearch.xml deleted file mode 100644 index 5872697..0000000 --- a/es/opensearch.xml +++ /dev/null @@ -1,11 +0,0 @@ - - - Blockchain Security Monitoring - Search Blockchain Security Monitoring - UTF-8 - https://docs.sakundi.io/es/img/favicon.ico - - - https://docs.sakundi.io/es/ - \ No newline at end of file diff --git a/es/search/index.html b/es/search/index.html deleted file mode 100644 index 985a2e7..0000000 --- a/es/search/index.html +++ /dev/null @@ -1,25 +0,0 @@ - - - - - -Búsqueda en la documentación | Blockchain Security Monitoring - - - - - - - - - -
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Búsqueda en la documentación

- - - - \ No newline at end of file diff --git a/es/sitemap.xml b/es/sitemap.xml index bd250ac..57215e3 100644 --- a/es/sitemap.xml +++ b/es/sitemap.xml @@ -1 +1 @@ -https://docs.sakundi.io/es/blogweekly0.5https://docs.sakundi.io/es/blog/archiveweekly0.5https://docs.sakundi.io/es/blog/introducing-tikunaweekly0.5https://docs.sakundi.io/es/blog/malicious-validatorsweekly0.5https://docs.sakundi.io/es/blog/tikuna-contributionweekly0.5https://docs.sakundi.io/es/blog/tikuna-final-blogpost-fase1weekly0.5https://docs.sakundi.io/es/markdown-pageweekly0.5https://docs.sakundi.io/es/searchweekly0.5https://docs.sakundi.io/es/docs/about/sakundiweekly0.5https://docs.sakundi.io/es/docs/about/teamweekly0.5https://docs.sakundi.io/es/docs/monitoring/beacon-nodeweekly0.5https://docs.sakundi.io/es/docs/monitoring/eclipse-attacksweekly0.5https://docs.sakundi.io/es/docs/monitoring/network-eth2weekly0.5https://docs.sakundi.io/es/docs/research/glossaryweekly0.5https://docs.sakundi.io/es/docs/research/introweekly0.5https://docs.sakundi.io/es/docs/research/referencesweekly0.5https://docs.sakundi.io/es/docs/research/stateoftheartweekly0.5https://docs.sakundi.io/es/docs/user-section/how-to-use-tikunaweekly0.5https://docs.sakundi.io/es/docs/user-section/understanding-dashboardsweekly0.5https://docs.sakundi.io/es/weekly0.5 \ No newline at end of file +https://docs.sakundi.io/es/blogweekly0.5https://docs.sakundi.io/es/blog/archiveweekly0.5https://docs.sakundi.io/es/blog/introducing-tikunaweekly0.5https://docs.sakundi.io/es/blog/malicious-validatorsweekly0.5https://docs.sakundi.io/es/blog/tikuna-contributionweekly0.5https://docs.sakundi.io/es/blog/tikuna-final-blogpost-fase1weekly0.5https://docs.sakundi.io/es/markdown-pageweekly0.5https://docs.sakundi.io/es/docs/about/sakundiweekly0.5https://docs.sakundi.io/es/docs/about/teamweekly0.5https://docs.sakundi.io/es/docs/monitoring/beacon-nodeweekly0.5https://docs.sakundi.io/es/docs/monitoring/eclipse-attacksweekly0.5https://docs.sakundi.io/es/docs/monitoring/network-eth2weekly0.5https://docs.sakundi.io/es/docs/research/glossaryweekly0.5https://docs.sakundi.io/es/docs/research/introweekly0.5https://docs.sakundi.io/es/docs/research/referencesweekly0.5https://docs.sakundi.io/es/docs/research/stateoftheartweekly0.5https://docs.sakundi.io/es/docs/user-section/how-to-use-tikunaweekly0.5https://docs.sakundi.io/es/docs/user-section/understanding-dashboardsweekly0.5https://docs.sakundi.io/es/weekly0.5 \ No newline at end of file diff --git a/img/favicon.ico b/img/favicon.ico index 968fbe3..5faefb5 100644 Binary files a/img/favicon.ico and b/img/favicon.ico differ diff --git a/img/sakundi-logo.png b/img/sakundi-logo.png new file mode 100644 index 0000000..ded6f3b Binary files /dev/null and b/img/sakundi-logo.png differ diff --git a/index.html b/index.html index c6fd14c..83743ee 100644 --- a/index.html +++ b/index.html @@ -3,23 +3,19 @@ -Blockchain Security Monitoring - - - - - - - +Blockchain Security Monitoring + + +
-

An AI-based Blockchain security monitoring system.

Here is how it works:

Research

Take a look at the main purposes behind the project

About Tikuna

Get to know about our team and involved companies

Blog

Go and read contents written by our team

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-

Markdown page example

You don't need React to write simple standalone pages.

+ + \ No newline at end of file diff --git a/opensearch.xml b/opensearch.xml deleted file mode 100644 index d8abc71..0000000 --- a/opensearch.xml +++ /dev/null @@ -1,11 +0,0 @@ - - - Blockchain Security Monitoring - Search Blockchain Security Monitoring - UTF-8 - https://docs.sakundi.io/img/favicon.ico - - - https://docs.sakundi.io/ - \ No newline at end of file diff --git a/search/index.html b/search/index.html deleted file mode 100644 index bd8d0bd..0000000 --- a/search/index.html +++ /dev/null @@ -1,25 +0,0 @@ - - - - - -Search the documentation | Blockchain Security Monitoring - - - - - - - - - -
-

Search the documentation

- - - - \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index 2d1bf0e..a725b76 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1 +1 @@ -https://docs.sakundi.io/blogweekly0.5https://docs.sakundi.io/blog/archiveweekly0.5https://docs.sakundi.io/blog/introducing-tikunaweekly0.5https://docs.sakundi.io/blog/malicious-validatorsweekly0.5https://docs.sakundi.io/blog/tikuna-contributionweekly0.5https://docs.sakundi.io/blog/tikuna-final-blogpost-fase1weekly0.5https://docs.sakundi.io/markdown-pageweekly0.5https://docs.sakundi.io/searchweekly0.5https://docs.sakundi.io/docs/about/sakundiweekly0.5https://docs.sakundi.io/docs/about/teamweekly0.5https://docs.sakundi.io/docs/monitoring/beacon-nodeweekly0.5https://docs.sakundi.io/docs/monitoring/eclipse-attacksweekly0.5https://docs.sakundi.io/docs/monitoring/network-eth2weekly0.5https://docs.sakundi.io/docs/research/glossaryweekly0.5https://docs.sakundi.io/docs/research/introweekly0.5https://docs.sakundi.io/docs/research/referencesweekly0.5https://docs.sakundi.io/docs/research/stateoftheartweekly0.5https://docs.sakundi.io/docs/user-section/how-to-use-tikunaweekly0.5https://docs.sakundi.io/docs/user-section/understanding-dashboardsweekly0.5https://docs.sakundi.io/weekly0.5 \ No newline at end of file +https://docs.sakundi.io/blogweekly0.5https://docs.sakundi.io/blog/archiveweekly0.5https://docs.sakundi.io/blog/introducing-tikunaweekly0.5https://docs.sakundi.io/blog/malicious-validatorsweekly0.5https://docs.sakundi.io/blog/tikuna-contributionweekly0.5https://docs.sakundi.io/blog/tikuna-final-blogpost-fase1weekly0.5https://docs.sakundi.io/markdown-pageweekly0.5https://docs.sakundi.io/docs/about/sakundiweekly0.5https://docs.sakundi.io/docs/about/teamweekly0.5https://docs.sakundi.io/docs/monitoring/beacon-nodeweekly0.5https://docs.sakundi.io/docs/monitoring/eclipse-attacksweekly0.5https://docs.sakundi.io/docs/monitoring/network-eth2weekly0.5https://docs.sakundi.io/docs/research/glossaryweekly0.5https://docs.sakundi.io/docs/research/introweekly0.5https://docs.sakundi.io/docs/research/referencesweekly0.5https://docs.sakundi.io/docs/research/stateoftheartweekly0.5https://docs.sakundi.io/docs/user-section/how-to-use-tikunaweekly0.5https://docs.sakundi.io/docs/user-section/understanding-dashboardsweekly0.5https://docs.sakundi.io/weekly0.5 \ No newline at end of file