GRU-DDoS2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine and Deep Learning for DDoS Detection\n",
    "### Marcos V. O. Assis (mvoassis@gmail.com)\n",
    "***\n",
    "\n",
    "> ## Published Results:\n",
    "\n",
    "* *A GRU deep learning system against attacks in software defined networks*\n",
    "\n",
    "* https://doi.org/10.1016/j.jnca.2020.102942\n",
    "\n",
    "\n",
    "\n",
    "* \\***Update - 06/2022** - improved detection results through better data cleaning process. Updated results on Git. \n",
    "\n",
    "> ## Objectives\n",
    "\n",
    "1. Evaluate different Machine and Deep Learning methods for anomaly detection.\n",
    "2. Detection of Distributed Denial of Service Attacks\n",
    "\n",
    "> ## Dataset\n",
    "\n",
    "* CIC-DDoS2019 - https://www.unb.ca/cic/datasets/ddos-2019.html\n",
    "\n",
    "> ## Evaluated Methods\n",
    "\n",
    "* Gated Recurrent Units (GRU)\n",
    "* Long-Short Term Memory (LSTM)\n",
    "* Convolutional Neural Network (CNN)\n",
    "* Deep Neural Network (DNN)\n",
    "* Support Vector Machine (SVM)\n",
    "* Logistic Regression (LR)\n",
    "* Gradient Descent (GD)\n",
    "* k Nearest Neighbors (kNN)\n",
    "\n",
    "> ## Environment Config.\n",
    "\n",
    "* Python 3.7.13\n",
    "* Numpy 1.16.4\n",
    "* Scikit-learn 0.21.2\n",
    "* Pandas 0.24.2\n",
    "* Tensorflow 1.14.0\n",
    "* Keras 2.2.4\n",
    "* Matplotlib 3.1.0\n",
    "* Seaborn 0.11.2\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing and treating CIC-DDoS-2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.utils import resample\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining functions to load files and downsample them\n",
    "\n",
    "As this research aims to develop a binary detector (Attack or Normal), we should balance the dataset between these two classes. However, CIC-DDOS2019 has few normal flows in it. Thus, downsampling is necessary.\n",
    "\n",
    "For the downsampling process, we allow anomalous flows to be \"mult\" times bigger than normal flows. This approach aims to reduce class disbalance while preventing information losses on attack flows (when the number of attack flows is downsampled to the number of normal ones, ML models could not fit appropriately).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mult = 5\n",
    "\n",
    "def load_file(path):\n",
    "    data = pd.read_csv(path, sep=',')\n",
    "\n",
    "    is_benign = data[' Label']=='BENIGN'\n",
    "    flows_ok = data[is_benign]\n",
    "    flows_ddos_full = data[~is_benign]\n",
    "    \n",
    "    sizeDownSample = len(flows_ok)*mult # tamanho do set final de dados anomalos\n",
    "    \n",
    "    # downsample majority\n",
    "    if (len(flows_ok)*mult) < (len(flows_ddos_full)): \n",
    "        flows_ddos_reduced = resample(flows_ddos_full,\n",
    "                                         replace = False, # sample without replacement\n",
    "                                         n_samples = sizeDownSample, # match minority n\n",
    "                                         random_state = 27) # reproducible results\n",
    "    else:\n",
    "        flows_ddos_reduced = flows_ddos_full\n",
    "    \n",
    "    return flows_ok, flows_ddos_reduced\n",
    "\n",
    " \n",
    "def load_huge_file(path):\n",
    "    df_chunk = pd.read_csv(path, chunksize=500000)\n",
    "    \n",
    "    chunk_list_ok = []  # append each chunk df here \n",
    "    chunk_list_ddos = [] \n",
    "\n",
    "    # Each chunk is in df format\n",
    "    for chunk in df_chunk:  \n",
    "        # perform data filtering \n",
    "        is_benign = chunk[' Label']=='BENIGN'\n",
    "        flows_ok = chunk[is_benign]\n",
    "        flows_ddos_full = chunk[~is_benign]\n",
    "        \n",
    "        if (len(flows_ok)*mult) < (len(flows_ddos_full)): \n",
    "            sizeDownSample = len(flows_ok)*mult # tamanho do set final de dados anomalos\n",
    "            \n",
    "            # downsample majority\n",
    "            flows_ddos_reduced = resample(flows_ddos_full,\n",
    "                                             replace = False, # sample without replacement\n",
    "                                             n_samples = sizeDownSample, # match minority n\n",
    "                                             random_state = 27) # reproducible results \n",
    "        else:\n",
    "            flows_ddos_reduced = flows_ddos_full\n",
    "            \n",
    "        # Once the data filtering is done, append the chunk to list\n",
    "        chunk_list_ok.append(flows_ok)\n",
    "        chunk_list_ddos.append(flows_ddos_reduced)\n",
    "        \n",
    "    # concat the list into dataframe \n",
    "    flows_ok = pd.concat(chunk_list_ok)\n",
    "    flows_ddos = pd.concat(chunk_list_ddos)\n",
    "    \n",
    "    return flows_ok, flows_ddos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading CIC-DDoS2019 - Day 1 (training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\mvoas\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3248: DtypeWarning: Columns (85) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file 1 loaded\n",
      "file 2 loaded\n",
      "file 3 loaded\n",
      "file 4 loaded\n",
      "file 5 loaded\n",
      "file 6 loaded\n",
      "file 7 loaded\n",
      "file 8 loaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\mvoas\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3248: DtypeWarning: Columns (21,85) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file 9 loaded\n",
      "file 10 loaded\n",
      "file 11 loaded\n"
     ]
    }
   ],
   "source": [
    "# file 1\n",
    "flows_ok, flows_ddos = load_huge_file('cicddos2019/01-12/TFTP.csv')\n",
    "print('file 1 loaded')\n",
    "\n",
    "# file 2\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_LDAP.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 2 loaded')\n",
    "\n",
    "# file 3\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_MSSQL.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 3 loaded')\n",
    "\n",
    "# file 4\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_NetBIOS.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 4 loaded')\n",
    "\n",
    "# file 5\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_NTP.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 5 loaded')\n",
    "\n",
    "# file 6\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_SNMP.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 6 loaded')\n",
    "\n",
    "# file 7\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_SSDP.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 7 loaded')\n",
    "\n",
    "# file 8\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_UDP.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 8 loaded')\n",
    "\n",
    "# file 9\n",
    "a,b = load_file('cicddos2019/01-12/Syn.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 9 loaded')\n",
    "\n",
    "# file 10\n",
    "a,b = load_file('cicddos2019/01-12/DrDoS_DNS.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 10 loaded')\n",
    "\n",
    "# file 11\n",
    "a,b = load_file('cicddos2019/01-12/UDPLag.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 11 loaded')\n",
    "\n",
    "del a,b\n",
    "\n",
    "samples = flows_ok.append(flows_ddos,ignore_index=True)\n",
    "samples.to_csv(r'cicddos2019/01-12/export_dataframe.csv', index = None, header=True) \n",
    "\n",
    "del flows_ddos, flows_ok"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading CIC-DDoS2019 - Day 2 (testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file 1 loaded\n",
      "file 2 loaded\n",
      "file 3 loaded\n",
      "file 4 loaded\n",
      "file 5 loaded\n"
     ]
    }
   ],
   "source": [
    "# file 1\n",
    "flows_ok, flows_ddos = load_file('cicddos2019/03-11/LDAP.csv')\n",
    "print('file 1 loaded')\n",
    "\n",
    "# file 2\n",
    "a,b = load_file('cicddos2019/03-11/MSSQL.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 2 loaded')\n",
    "\n",
    "# file 3\n",
    "a,b = load_file('cicddos2019/03-11/NetBIOS.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 3 loaded')\n",
    "\n",
    "# file 4\n",
    "a,b = load_file('cicddos2019/03-11/PortMap.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 4 loaded')\n",
    "\n",
    "# file 5\n",
    "a,b = load_file('cicddos2019/03-11/Syn.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 5 loaded')\n",
    "'''\n",
    "# following files won't load**\n",
    "# file 6\n",
    "\n",
    "a,b = load_file('cicddos2019/03-11/UDP.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 6 loaded')\n",
    "\n",
    "# file 7\n",
    "a,b = load_file('cicddos2019/03-11/UDPLag.csv')\n",
    "flows_ok = flows_ok.append(a,ignore_index=True)\n",
    "flows_ddos = flows_ddos.append(b,ignore_index=True)\n",
    "print('file 7 loaded')\n",
    "'''\n",
    "tests = flows_ok.append(flows_ddos,ignore_index=True)\n",
    "tests.to_csv(r'cicddos2019/01-12/export_tests.csv', index = None, header=True) \n",
    "\n",
    "del flows_ddos, flows_ok, a, b\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIC-DDoS2019 Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\PaperGRU\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3186: DtypeWarning: Columns (21,22,85) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data processed\n"
     ]
    }
   ],
   "source": [
    "# training data\n",
    "samples = pd.read_csv('cicddos2019/01-12/export_dataframe.csv', sep=',')\n",
    "\n",
    "def string2numeric_hash(text):\n",
    "    import hashlib\n",
    "    return int(hashlib.md5(text).hexdigest()[:8], 16)\n",
    "\n",
    "# Flows Packet/s e Bytes/s - Replace infinity by 0\n",
    "samples = samples.replace('Infinity','0')\n",
    "samples = samples.replace(np.inf,0)\n",
    "#samples = samples.replace('nan','0')\n",
    "samples[' Flow Packets/s'] = pd.to_numeric(samples[' Flow Packets/s'])\n",
    "\n",
    "samples['Flow Bytes/s'] = samples['Flow Bytes/s'].fillna(0)\n",
    "samples['Flow Bytes/s'] = pd.to_numeric(samples['Flow Bytes/s'])\n",
    "\n",
    "\n",
    "#Label\n",
    "samples[' Label'] = samples[' Label'].replace('BENIGN',0)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_DNS',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_LDAP',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_MSSQL',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_NTP',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_NetBIOS',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_SNMP',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_SSDP',1)\n",
    "samples[' Label'] = samples[' Label'].replace('DrDoS_UDP',1)\n",
    "samples[' Label'] = samples[' Label'].replace('Syn',1)\n",
    "samples[' Label'] = samples[' Label'].replace('TFTP',1)\n",
    "samples[' Label'] = samples[' Label'].replace('UDP-lag',1)\n",
    "samples[' Label'] = samples[' Label'].replace('WebDDoS',1)\n",
    "\n",
    "#Timestamp - Drop day, then convert hour, minute and seconds to hashing \n",
    "colunaTime = pd.DataFrame(samples[' Timestamp'].str.split(' ',1).tolist(), columns = ['dia','horas'])\n",
    "colunaTime = pd.DataFrame(colunaTime['horas'].str.split('.',1).tolist(),columns = ['horas','milisec'])\n",
    "stringHoras = pd.DataFrame(colunaTime['horas'].str.encode('utf-8'))\n",
    "samples[' Timestamp'] = pd.DataFrame(stringHoras['horas'].apply(string2numeric_hash))#colunaTime['horas']\n",
    "del colunaTime,stringHoras\n",
    "\n",
    "\n",
    "# flowID - IP origem - IP destino - Simillar HTTP -> Drop (individual flow analysis)\n",
    "del samples[' Source IP']\n",
    "del samples[' Destination IP']\n",
    "del samples['Flow ID']\n",
    "del samples['SimillarHTTP']\n",
    "del samples['Unnamed: 0']\n",
    "\n",
    "samples.to_csv(r'cicddos2019/01-12/export_dataframe_proc.csv', index = None, header=True) \n",
    "print('Training data processed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\PaperGRU\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3186: DtypeWarning: Columns (85) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data processed\n"
     ]
    }
   ],
   "source": [
    "# test data\n",
    "tests = pd.read_csv('cicddos2019/01-12/export_tests.csv', sep=',')\n",
    "\n",
    "def string2numeric_hash(text):\n",
    "    import hashlib\n",
    "    return int(hashlib.md5(text).hexdigest()[:8], 16)\n",
    "\n",
    "# Flows Packet/s e Bytes/s - Change infinity by 0\n",
    "tests = tests.replace('Infinity','0')\n",
    "tests = tests.replace(np.inf,0)\n",
    "#amostras = amostras.replace('nan','0')\n",
    "tests[' Flow Packets/s'] = pd.to_numeric(tests[' Flow Packets/s'])\n",
    "\n",
    "tests['Flow Bytes/s'] = tests['Flow Bytes/s'].fillna(0)\n",
    "tests['Flow Bytes/s'] = pd.to_numeric(tests['Flow Bytes/s'])\n",
    "\n",
    "\n",
    "#Label\n",
    "tests[' Label'] = tests[' Label'].replace('BENIGN',0)\n",
    "tests[' Label'] = tests[' Label'].replace('LDAP',1)\n",
    "tests[' Label'] = tests[' Label'].replace('NetBIOS',1)\n",
    "tests[' Label'] = tests[' Label'].replace('MSSQL',1)\n",
    "tests[' Label'] = tests[' Label'].replace('Portmap',1)\n",
    "tests[' Label'] = tests[' Label'].replace('Syn',1)\n",
    "#tests[' Label'] = tests[' Label'].replace('DrDoS_SNMP',1)\n",
    "#tests[' Label'] = tests[' Label'].replace('DrDoS_SSDP',1)\n",
    "\n",
    "#Timestamp - Drop day, then convert hour, minute and seconds to hashing \n",
    "colunaTime = pd.DataFrame(tests[' Timestamp'].str.split(' ',1).tolist(), columns = ['dia','horas'])\n",
    "colunaTime = pd.DataFrame(colunaTime['horas'].str.split('.',1).tolist(),columns = ['horas','milisec'])\n",
    "stringHoras = pd.DataFrame(colunaTime['horas'].str.encode('utf-8'))\n",
    "tests[' Timestamp'] = pd.DataFrame(stringHoras['horas'].apply(string2numeric_hash))#colunaTime['horas']\n",
    "del colunaTime,stringHoras\n",
    "\n",
    "# flowID - IP origem - IP destino - Simillar HTTP -> Deletar (analise fluxo a fluxo)\n",
    "del tests[' Source IP']\n",
    "del tests[' Destination IP']\n",
    "del tests['Flow ID']\n",
    "del tests['SimillarHTTP']\n",
    "del tests['Unnamed: 0']\n",
    "\n",
    "tests.to_csv(r'cicddos2019/01-12/export_tests_proc.csv', index = None, header=True) \n",
    "print('Test data processed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods implementation\n",
    "\n",
    "Importing required library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "from keras.models import Sequential\n",
    "\n",
    "from keras.layers import Dense,GRU,Embedding,Dropout,Flatten,Conv1D,MaxPooling1D,LSTM\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gated Recurrent Units (GRU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#input_size\n",
    "# -> CIC-DDoS2019 82\n",
    "# -> CIC-IDS2018 78\n",
    "\n",
    "def GRU_model(input_size):\n",
    "   \n",
    "    # Initialize the constructor\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(GRU(32, input_shape=(input_size,1), return_sequences=False)) #\n",
    "    model.add(Dropout(0.5))    \n",
    "    model.add(Dense(10, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    model.build()\n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional Neural Network (CNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CNN_model(input_size):\n",
    "   \n",
    "    # Initialize the constructor\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv1D(filters=64, kernel_size=8, activation='relu', input_shape=(input_size,1)))\n",
    "    model.add(MaxPooling1D(2))\n",
    "    model.add(Conv1D(filters=32, kernel_size=16, activation='relu'))\n",
    "    model.add(MaxPooling1D(2))\n",
    "    model.add(Conv1D(filters=16, kernel_size=3, activation='relu'))\n",
    "    model.add(MaxPooling1D(2))\n",
    "    \n",
    "    model.add(Dropout(0.5))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(10, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long-Short Term Memory (LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM_model(input_size):\n",
    "   \n",
    "    # Initialize the constructor\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(LSTM(32,input_shape=(input_size,1), return_sequences=False))\n",
    "    model.add(Dropout(0.5))    \n",
    "    model.add(Dense(10, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Neural Network (DNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DNN_model(input_size):\n",
    "   \n",
    "    # Initialize the constructor\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Dense(2, activation='relu', input_shape=(input_size,)))\n",
    "    #model.add(Dense(100, activation='relu'))   \n",
    "    #model.add(Dense(40, activation='relu'))\n",
    "    #model.add(Dense(10, activation='relu'))\n",
    "    #model.add(Dropout(0.5))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SVM():\n",
    "    return SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression (LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LR():\n",
    "    return LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Descent (GD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GD():\n",
    "    return SGDClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k Nearest Neighbors (kNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kNN():\n",
    "    return KNeighborsClassifier(n_neighbors=3, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auxiliar Functions\n",
    "\n",
    "Implementation of auxiliar functions, such as testing, compiling/training, 3d reshape, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train_test(samples)\n",
    "> Receives a group of samples and split it in train/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def train_test(samples):\n",
    "    # Import `train_test_split` from `sklearn.model_selection`\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    import numpy as np\n",
    "    \n",
    "    # Specify the data \n",
    "    X=samples.iloc[:,0:(samples.shape[1]-1)]\n",
    "    \n",
    "    # Specify the target labels and flatten the array\n",
    "    #y= np.ravel(amostras.type)\n",
    "    y= samples.iloc[:,-1]\n",
    "    \n",
    "    # Split the data up in train and test sets\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### normalize_data(X_train,X_test)\n",
    "\n",
    "> Normalize data between -1 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize input data\n",
    "\n",
    "def normalize_data(X_train,X_test):\n",
    "    # Import `StandardScaler` from `sklearn.preprocessing`\n",
    "    from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "    \n",
    "    # Define the scaler \n",
    "    #scaler = StandardScaler().fit(X_train)\n",
    "    scaler = MinMaxScaler(feature_range=(-1, 1)).fit(X_train)\n",
    "    \n",
    "    # Scale the train set\n",
    "    X_train = scaler.transform(X_train)\n",
    "    \n",
    "    # Scale the test set\n",
    "    X_test = scaler.transform(X_test)\n",
    "    \n",
    "    return X_train, X_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### format_{2,3}d()\n",
    "\n",
    "> Reshape data in 3d or 2d format (for input in methods such as GRU, CNN and LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape data input\n",
    "\n",
    "def format_3d(df):\n",
    "    \n",
    "    X = np.array(df)\n",
    "    return np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
    "\n",
    "def format_2d(df):\n",
    "    \n",
    "    X = np.array(df)\n",
    "    return np.reshape(X, (X.shape[0], X.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compile_train(model,X_train,y_train,deep=True)\n",
    "\n",
    "> Compile and train learning model\n",
    "\n",
    "> deep = False for scikit-learn ML methods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile and train learning model\n",
    "\n",
    "def compile_train(model,X_train,y_train,deep=True):\n",
    "    \n",
    "    if(deep==True):\n",
    "        import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "        model.compile(loss='binary_crossentropy',\n",
    "                      optimizer='adam',\n",
    "                      metrics=['accuracy'])\n",
    "        \n",
    "        history = model.fit(X_train, y_train,epochs=10, batch_size=256, verbose=1)\n",
    "        #model.fit(X_train, y_train,epochs=3)\n",
    "\n",
    "        # summarize history for accuracy\n",
    "        plt.plot(history.history['acc'])\n",
    "        plt.title('model accuracy')\n",
    "        plt.ylabel('accuracy')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train'], loc='upper left')\n",
    "        plt.show()\n",
    "        # summarize history for loss\n",
    "        plt.plot(history.history['loss'])\n",
    "        plt.title('model loss')\n",
    "        plt.ylabel('loss')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train'], loc='upper left')\n",
    "        plt.show()\n",
    "\n",
    "        print(model.metrics_names)\n",
    "    \n",
    "    else:\n",
    "        model.fit(X_train, y_train) #SVM, LR, GD\n",
    "    \n",
    "    print('Model Compiled and Trained')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### testes(model,X_test,y_test,y_pred, deep=True)\n",
    "\n",
    "> Testing performance outcomes of the methods\n",
    "\n",
    "> deep = False for scikit-learn ML methods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing performance outcomes of the methods\n",
    "\n",
    "def testes(model,X_test,y_test,y_pred, deep=True):\n",
    "    if(deep==True): \n",
    "        score = model.evaluate(X_test, y_test,verbose=1)\n",
    "\n",
    "        print(score)\n",
    "    \n",
    "    # Alguns testes adicionais\n",
    "    #y_test = formatar2d(y_test)\n",
    "    #y_pred = formatar2d(y_pred)\n",
    "    \n",
    "    \n",
    "    # Import the modules from `sklearn.metrics`\n",
    "    from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, cohen_kappa_score, accuracy_score\n",
    "    \n",
    "    # Accuracy \n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    print('\\nAccuracy')\n",
    "    print(acc)\n",
    "    \n",
    "    # Precision \n",
    "    prec = precision_score(y_test, y_pred)#,average='macro')\n",
    "    print('\\nPrecision')\n",
    "    print(prec)\n",
    "    \n",
    "    # Recall\n",
    "    rec = recall_score(y_test, y_pred) #,average='macro')\n",
    "    print('\\nRecall')\n",
    "    print(rec)\n",
    "    \n",
    "    # F1 score\n",
    "    f1 = f1_score(y_test,y_pred) #,average='macro')\n",
    "    print('\\nF1 Score')\n",
    "    print(f1)\n",
    "    \n",
    "    #average\n",
    "    avrg = (acc+prec+rec+f1)/4\n",
    "    print('\\nAverage (acc, prec, rec, f1)')\n",
    "    print(avrg)\n",
    "    \n",
    "    return acc, prec, rec, f1, avrg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test_normal_atk(y_test,y_pred):\n",
    "\n",
    "> Calculate the correct classification rate of normal and attack flow records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_normal_atk(y_test,y_pred):\n",
    "    df = pd.DataFrame()\n",
    "    df['y_test'] = y_test\n",
    "    df['y_pred'] = y_pred\n",
    "    \n",
    "    normal = len(df.query('y_test == 0'))\n",
    "    atk = len(y_test)-normal\n",
    "    \n",
    "    wrong = df.query('y_test != y_pred')\n",
    "    \n",
    "    normal_detect_rate = (normal - wrong.groupby('y_test').count().iloc[0][0]) / normal\n",
    "    atk_detect_rate = (atk - wrong.groupby('y_test').count().iloc[1][0]) / atk\n",
    "    \n",
    "    #print(normal_detect_rate,atk_detect_rate)\n",
    "    \n",
    "    return normal_detect_rate, atk_detect_rate\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving and Loading methods\n",
    "\n",
    "> Methods for saving and loading trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model and weights\n",
    "\n",
    "def save_model(model,name):\n",
    "    from keras.models import model_from_json\n",
    "    \n",
    "    arq_json = 'Models/' + name + '.json'\n",
    "    model_json = model.to_json()\n",
    "    with open(arq_json,\"w\") as json_file:\n",
    "        json_file.write(model_json)\n",
    "    \n",
    "    arq_h5 = 'Models/' + name + '.h5'\n",
    "    model.save_weights(arq_h5)\n",
    "    print('Model Saved')\n",
    "    \n",
    "def load_model(name):\n",
    "    from keras.models import model_from_json\n",
    "    \n",
    "    arq_json = 'Models/' + name + '.json'\n",
    "    json_file = open(arq_json,'r')\n",
    "    loaded_model_json = json_file.read()\n",
    "    json_file.close()\n",
    "    loaded_model = model_from_json(loaded_model_json)\n",
    "    \n",
    "    arq_h5 = 'Models/' + name + '.h5'\n",
    "    loaded_model.load_weights(arq_h5)\n",
    "    \n",
    "    print('Model loaded')\n",
    "    \n",
    "    return loaded_model\n",
    "\n",
    "def save_Sklearn(model,nome):\n",
    "    import pickle\n",
    "    arquivo = 'Models/'+ nome + '.pkl'\n",
    "    with open(arquivo,'wb') as file:\n",
    "        pickle.dump(model,file)\n",
    "    print('Model sklearn saved')\n",
    "\n",
    "def load_Sklearn(nome):\n",
    "    import pickle\n",
    "    arquivo = 'Models/'+ nome + '.pkl'\n",
    "    with open(arquivo,'rb') as file:\n",
    "        model = pickle.load(file)\n",
    "    print('Model sklearn loaded')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main script for testing the learning methods\n",
    "\n",
    "> **Dataset - CIC-DDoS2019**\n",
    "\n",
    "Loading training dataset (day 1), upsampling normal flows for balancing the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UPSAMPLE OF NORMAL FLOWS\n",
    "    \n",
    "samples = pd.read_csv('cicddos2019/01-12/export_dataframe_proc.csv', sep=',')\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test(samples)\n",
    "\n",
    "\n",
    "#junta novamente pra aumentar o numero de normais\n",
    "X = pd.concat([X_train, y_train], axis=1)\n",
    "\n",
    "# separate minority and majority classes\n",
    "is_benign = X[' Label']==0 #base de dados toda junta\n",
    "\n",
    "normal = X[is_benign]\n",
    "ddos = X[~is_benign]\n",
    "\n",
    "# upsample minority\n",
    "normal_upsampled = resample(normal,\n",
    "                          replace=True, # sample with replacement\n",
    "                          n_samples=len(ddos), # match number in majority class\n",
    "                          random_state=27) # reproducible results\n",
    "\n",
    "# combine majority and upsampled minority\n",
    "upsampled = pd.concat([normal_upsampled, ddos])\n",
    "\n",
    "# Specify the data \n",
    "X_train=upsampled.iloc[:,0:(upsampled.shape[1]-1)]    #DDoS\n",
    "y_train= upsampled.iloc[:,-1]  #DDoS\n",
    "\n",
    "input_size = (X_train.shape[1], 1)\n",
    "\n",
    "del X, normal_upsampled, ddos, upsampled, normal #, l1, l2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the test dataset (day 2) and normalizing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "tests = pd.read_csv('cicddos2019/01-12/export_tests_proc.csv', sep=',')\n",
    "\n",
    "# X_test = np.concatenate((X_test,(tests.iloc[:,0:(tests.shape[1]-1)]).to_numpy())) # testar 33% + dia de testes\n",
    "# y_test = np.concatenate((y_test,tests.iloc[:,-1]))\n",
    "\n",
    "del X_test,y_test                            # testar só o dia de testes\n",
    "X_test = tests.iloc[:,0:(tests.shape[1]-1)]                        \n",
    "y_test = tests.iloc[:,-1]\n",
    "\n",
    "# print((y_test.shape))\n",
    "# print((X_test.shape))\n",
    "\n",
    "X_train, X_test = normalize_data(X_train,X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compiling and Training the methods\n",
    "\n",
    "> Comment the last 2 code blocks\n",
    "\n",
    "**OR**\n",
    "\n",
    "Loading and compiling the methods\n",
    "\n",
    "> Comment the first 2 code blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded\n",
      "Model loaded\n",
      "Model loaded\n",
      "Model loaded\n",
      "Model sklearn loaded\n",
      "Model sklearn loaded\n",
      "Model sklearn loaded\n",
      "Model sklearn loaded\n"
     ]
    }
   ],
   "source": [
    "\n",
    "## Comment next 2 blocks if loading pre-trained models\n",
    "## Execute them if training new models\n",
    "\n",
    "# model_gru = GRU_model() #quando treina novo modelo\n",
    "# model_cnn = CNN_model()\n",
    "# model_lstm = LSTM_model()\n",
    "# model_dnn = DNN_model(X_train.shape[1])\n",
    "# model_svm = SVM()\n",
    "# model_lr = LR()\n",
    "# model_gd = GD()\n",
    "# model_knn = kNN()\n",
    " \n",
    "# model_gru = compile_train(model_gru,format_3d(X_train),y_train)  #quando treina novo modelo, ou retreina\n",
    "# model_cnn = compile_train(model_cnn,format_3d(X_train),y_train)\n",
    "# model_lstm = compile_train(model_lstm,format_3d(X_train),y_train)\n",
    "# model_dnn = compile_train(model_dnn,X_train,y_train)\n",
    "# model_svm = compile_train(model_svm,X_train,y_train,False)\n",
    "# model_lr = compile_train(model_lr,X_train,y_train,False)\n",
    "# model_gd = compile_train(model_gd,X_train,y_train,False)\n",
    "# model_knn = compile_train(model_knn,X_train,y_train,False)\n",
    "\n",
    "## Comment next 2 blocks if training new models\n",
    "## Execute them if loading pre-trained models\n",
    "\n",
    "model_gru = load_model('GRU20-32-b256') #when loading previously saved trained model and weights\n",
    "model_cnn = load_model('CNN5-3cam-b2560')\n",
    "model_lstm = load_model('LSTM5-32-b256')\n",
    "model_dnn = load_model('DNN5-2560')\n",
    "model_svm = load_Sklearn('SVM') \n",
    "model_lr = load_Sklearn('LR')\n",
    "model_gd = load_Sklearn('GD')\n",
    "model_knn = load_Sklearn('kNN-1viz')\n",
    "\n",
    "model_gru.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) #qdo carrega modelo salvo\n",
    "model_cnn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model_dnn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing CIC-DDoS2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(columns=['Method','Accuracy','Precision','Recall', 'F1_Score', 'Average','Normal_Detect_Rate','Atk_Detect_Rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298578/298578 [==============================] - 76s 253us/step\n",
      "[0.01105657341191673, 0.9985832847698088]\n",
      "\n",
      "Accuracy\n",
      "0.9985832847698088\n",
      "\n",
      "Precision\n",
      "0.9995093228655545\n",
      "\n",
      "Recall\n",
      "0.9987902658601773\n",
      "\n",
      "F1 Score\n",
      "0.9991496649921299\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9990081346219176\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_gru.predict(format_3d(X_test)) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_gru,format_3d(X_test),y_test,y_pred)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'GRU', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298578/298578 [==============================] - 54s 182us/step\n",
      "[0.029455807720052126, 0.9877251505469258]\n",
      "\n",
      "Accuracy\n",
      "0.9877251505469258\n",
      "\n",
      "Precision\n",
      "0.9942101910314407\n",
      "\n",
      "Recall\n",
      "0.9910415368848341\n",
      "\n",
      "F1 Score\n",
      "0.9926233352185928\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9914000534204483\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_cnn.predict(format_3d(X_test)) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_cnn,format_3d(X_test),y_test,y_pred)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'CNN', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298578/298578 [==============================] - 85s 283us/step\n",
      "[0.07657973352144937, 0.978297128388961]\n",
      "\n",
      "Accuracy\n",
      "0.9782971283885618\n",
      "\n",
      "Precision\n",
      "0.9991801691570573\n",
      "\n",
      "Recall\n",
      "0.9747563450756587\n",
      "\n",
      "F1 Score\n",
      "0.9868171572257439\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9847626999617554\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_lstm.predict(format_3d(X_test)) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_lstm,format_3d(X_test),y_test,y_pred)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'LSTM', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298578/298578 [==============================] - 15s 50us/step\n",
      "[0.043137314041730386, 0.9974412046433427]\n",
      "\n",
      "Accuracy\n",
      "0.9974412046433427\n",
      "\n",
      "Precision\n",
      "0.9981403904778353\n",
      "\n",
      "Recall\n",
      "0.9987902658601773\n",
      "\n",
      "F1 Score\n",
      "0.9984652224222165\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9982092708508931\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_dnn.predict(X_test) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_dnn,X_test,y_test,y_pred)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'DNN', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy\n",
      "0.9986502689414491\n",
      "\n",
      "Precision\n",
      "0.9991480332427783\n",
      "\n",
      "Recall\n",
      "0.9992323613930029\n",
      "\n",
      "F1 Score\n",
      "0.9991901955386405\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9990552147789676\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_svm.predict(X_test) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_svm,X_test,y_test,y_pred,False)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'SVM', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy\n",
      "0.9985799355612269\n",
      "\n",
      "Precision\n",
      "0.9990677302043358\n",
      "\n",
      "Recall\n",
      "0.9992283423427044\n",
      "\n",
      "F1 Score\n",
      "0.9991480298189563\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9990060094818058\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_lr.predict(X_test) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_lr,X_test,y_test,y_pred,False)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'LR', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy\n",
      "0.9986804118186873\n",
      "\n",
      "Precision\n",
      "0.9992042344373576\n",
      "\n",
      "Recall\n",
      "0.9992122661415107\n",
      "\n",
      "F1 Score\n",
      "0.9992082502732943\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9990762906677125\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_gd.predict(X_test) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_gd,X_test,y_test,y_pred,False)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'GB', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\PaperGRU\\lib\\site-packages\\sklearn\\neighbors\\base.py:441: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  old_joblib = LooseVersion(joblib_version) < LooseVersion('0.12')\n",
      "D:\\ProgramData\\Anaconda3\\envs\\PaperGRU\\lib\\site-packages\\sklearn\\neighbors\\base.py:441: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  old_joblib = LooseVersion(joblib_version) < LooseVersion('0.12')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy\n",
      "0.9991258565600949\n",
      "\n",
      "Precision\n",
      "0.9995578316061968\n",
      "\n",
      "Recall\n",
      "0.9993931234049395\n",
      "\n",
      "F1 Score\n",
      "0.999475470719811\n",
      "\n",
      "Average (acc, prec, rec, f1)\n",
      "0.9993880705727606\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_knn.predict(X_test) \n",
    "\n",
    "y_pred = y_pred.round()\n",
    " \n",
    "acc, prec, rec, f1, avrg = testes(model_knn,X_test,y_test,y_pred,False)\n",
    "\n",
    "norm, atk = test_normal_atk(y_test,y_pred)\n",
    "\n",
    "results = results.append({'Method':'kNN', 'Accuracy':acc, 'Precision':prec, 'F1_Score':f1,\n",
    "                          'Recall':rec,'Average':avrg, 'Normal_Detect_Rate':norm, 'Atk_Detect_Rate':atk}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion and Results\n",
    "\n",
    "Showing the table 'results', containing the performance metrics outcomes for each method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1_Score</th>\n",
       "      <th>Average</th>\n",
       "      <th>Normal_Detect_Rate</th>\n",
       "      <th>Atk_Detect_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GRU</td>\n",
       "      <td>0.998583</td>\n",
       "      <td>0.999509</td>\n",
       "      <td>0.998790</td>\n",
       "      <td>0.999150</td>\n",
       "      <td>0.999008</td>\n",
       "      <td>0.997548</td>\n",
       "      <td>0.998790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CNN</td>\n",
       "      <td>0.987725</td>\n",
       "      <td>0.994210</td>\n",
       "      <td>0.991042</td>\n",
       "      <td>0.992623</td>\n",
       "      <td>0.991400</td>\n",
       "      <td>0.971143</td>\n",
       "      <td>0.991042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>0.978297</td>\n",
       "      <td>0.999180</td>\n",
       "      <td>0.974756</td>\n",
       "      <td>0.986817</td>\n",
       "      <td>0.984763</td>\n",
       "      <td>0.996001</td>\n",
       "      <td>0.974756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DNN</td>\n",
       "      <td>0.997441</td>\n",
       "      <td>0.998140</td>\n",
       "      <td>0.998790</td>\n",
       "      <td>0.998465</td>\n",
       "      <td>0.998209</td>\n",
       "      <td>0.990696</td>\n",
       "      <td>0.998790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.998650</td>\n",
       "      <td>0.999148</td>\n",
       "      <td>0.999232</td>\n",
       "      <td>0.999190</td>\n",
       "      <td>0.999055</td>\n",
       "      <td>0.995740</td>\n",
       "      <td>0.999232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.998580</td>\n",
       "      <td>0.999068</td>\n",
       "      <td>0.999228</td>\n",
       "      <td>0.999148</td>\n",
       "      <td>0.999006</td>\n",
       "      <td>0.995338</td>\n",
       "      <td>0.999228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GB</td>\n",
       "      <td>0.998680</td>\n",
       "      <td>0.999204</td>\n",
       "      <td>0.999212</td>\n",
       "      <td>0.999208</td>\n",
       "      <td>0.999076</td>\n",
       "      <td>0.996021</td>\n",
       "      <td>0.999212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kNN</td>\n",
       "      <td>0.999126</td>\n",
       "      <td>0.999558</td>\n",
       "      <td>0.999393</td>\n",
       "      <td>0.999475</td>\n",
       "      <td>0.999388</td>\n",
       "      <td>0.997790</td>\n",
       "      <td>0.999393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Method  Accuracy  Precision    Recall  F1_Score   Average  \\\n",
       "0    GRU  0.998583   0.999509  0.998790  0.999150  0.999008   \n",
       "1    CNN  0.987725   0.994210  0.991042  0.992623  0.991400   \n",
       "2   LSTM  0.978297   0.999180  0.974756  0.986817  0.984763   \n",
       "3    DNN  0.997441   0.998140  0.998790  0.998465  0.998209   \n",
       "4    SVM  0.998650   0.999148  0.999232  0.999190  0.999055   \n",
       "5     LR  0.998580   0.999068  0.999228  0.999148  0.999006   \n",
       "6     GB  0.998680   0.999204  0.999212  0.999208  0.999076   \n",
       "7    kNN  0.999126   0.999558  0.999393  0.999475  0.999388   \n",
       "\n",
       "   Normal_Detect_Rate  Atk_Detect_Rate  \n",
       "0            0.997548         0.998790  \n",
       "1            0.971143         0.991042  \n",
       "2            0.996001         0.974756  \n",
       "3            0.990696         0.998790  \n",
       "4            0.995740         0.999232  \n",
       "5            0.995338         0.999228  \n",
       "6            0.996021         0.999212  \n",
       "7            0.997790         0.999393  "
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification Metrics:**\n",
    "* Accuracy\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 Measure (F1 Score)\n",
    "\n",
    "Showing performance outcomes of the methods: \n",
    "* GRU\n",
    "* DNN\n",
    "* SVM\n",
    "* LR\n",
    "* GB\n",
    "* kNN\n",
    "\n",
    "LSTM and CNN were separated for visualization improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.catplot(data=results.iloc[:,:5].query('Method != \"LSTM\" and Method != \"CNN\"'), col='Method', col_wrap=3, kind='bar', height=3, aspect=2)\n",
    "ax.set(ylim=(0.99,1))\n",
    "ax.set_xticklabels(rotation=45)\n",
    "ax = ax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As observed, the evaluated methods achieved good performance outcomes close to 1. As the evaluated methods achieved similar outcomes, a more specific analysis should be performed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing results of the LSTM and CNN methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.catplot(data=results.iloc[:,:5].query('Method == \"LSTM\" or Method == \"CNN\"'), col='Method', col_wrap=3, kind='bar', height=3, aspect=2)\n",
    "ax.set(ylim=(0.97,1))\n",
    "ax.set_xticklabels(rotation=45)\n",
    "ax = ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> CNN and LSTM fared worse between the tested approaches. However, these methods achieved performance metrics above 97%, which is a relatively good outcome. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Average of the Metrics**\n",
    "\n",
    "Plotting the Average of the previously mentioned performance metrics to summarize the method's results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HR74146VLT+X/KjQ0VJ988oluvfVWzZkzR++9954qVKiguXPnFrj/5apUqaJ58+bp9ttv15w5czRjxgzFx8drxowZ+sc//qHDhw/bVmzp1KmT/vnPfyonJ0fTp0/XqlWrNH78eNWvX9+ukA8MDNTixYtVv359LViwQNOnT9eFCxc0ZcoUDR069Krx5B1br149ffLJJ3rvvffk5eWl999/X5GRkVc9tiDNmzdX586dtWnTJr3++uv5fpnT1dSsWVNLlixRmzZttGTJEr355ps6fvy4xo8fr1deeeW6Y8l7R6BVq1ZXneJU2HFr1aqlyMhI7du3T2+++eZ1vfTr4eGhOXPmaOjQodq+fbveeOMN7du3Ty+//LKeeuopSZdWuJk7d67KlSunOXPmaMqUKTp37pxmzJih7t27X/f5AwCA4sFirvYmI24qubm5OnfunO3J9uXCw8Pl4+Nj+2VMAAAAKL5K5JN4d5Wbm6vWrVvr5Zdfttt+8OBBHTp0SA0bNnRRZAAAAChKJXJOvLsqU6aMOnXqpGXLlslisah+/fo6c+aMFi9eLF9fXw0ePNjVIQIAAKAIMJ3GzWRmZmr+/PmKjo7WyZMnVbFiRTVv3lzPPvusatSo4erwAAAAUAQo4gEAAAA3w5x4AAAAwM1QxAMAAABupkS+2JqSkiGrlVlEjuDn562kpHRXh1EskEvHIp+ORT4di3w6Drl0LPLpOB4eFvn6VnBa/yWyiLdaDUW8A5FLxyGXjkU+HYt8Ohb5dBxy6Vjk0z3wYisAAACKlcyL2UpLzXRpDB4eFvn5eTut/xL5JH7kWyuUmJLh6jAAAADgBF9MG6A0ubaIdzZebAUAAADcDEU8AAAA4GYo4gEAAAA3QxEPAAAAuBmKeAAAAMDNFPnqNDk5Ofroo48UHR0ti8Wi3Nxc9ejRQ8OGDdPs2bP173//W1WqVJEkZWVlydPTU6+++qruv/9+zZo1S5L09NNP2/pbvny5du7cqSlTphT1qQAAAAAuUeRF/KRJk5SYmKj//Oc/8vHxUXp6ukaMGKGKFStKkvr162dXpC9cuFBTpkzR0qVLizpUAAAA4KZUpEX8qVOnFB0drc2bN8vHx0eS5O3trZdfflmHDx9WcnKy3f5Wq1WnTp3SLbfcUpRhAgAAADe1Ii3i9+zZo1q1auUrymvVqqVatWrp4MGD+ve//62YmBilpqbKarWqTZs2evPNN4syTAAAAOCmVuTTaSwWi+3va9as0dy5c2W1WlWmTBm1adPGNp0mISFBDz/8sBo1aqSqVavajjXG2PVnjLHrEwAAACjuinR1mvr16+v3339Xenq6JKlTp06KiorS3LlzlZKSYrevv7+/Jk+erNdee03Hjx+XJN1yyy1KTU212y8pKYnpNgAAAChRirSIv+2229StWzeNHTvWVozn5OQoNjZWHh75Q2ncuLHatGmj6dOnS5KCgoIUGxtrmzuflpam1atXq3nz5kV3EgAAAICLFfl0mldffVULFizQoEGDlJubq4yMDAUFBemjjz7SqlWr8u0/atQohYWFadeuXWrSpImGDRumRx55RJKUm5urPn36KDg4uIjPAgAAAHAdi/nrJPMSYORbK5SYkuHqMAAAAOAEX0wboISENJfG4OFhkZ+ft/P6d1rPAAAAAJyCIh4AAABwMxTxAAAAgJuhiAcAAADcDEU8AAAA4GZK5Oo0AAAAKL4yL2YrLTXTpTE4e3WaIl8n/maQlJQuq5XvLo7g71/R5Us4FRfk0rHIp2ORT8cin45DLh2LfLoPptMAAAAAboYiHgAAAHAzFPEAAACAm6GIBwAAANxMiXyx1ZlvCpdE/v4VXR1CsUEuHYt8Opa75jMn66JSzmW5OgwAcKgSWcTv/WCsslKTXB0GAKAI3D/mY0kU8QCKF6bTAAAAAG6GIh4AAABwMxTxAAAAgJuhiAcAAADcDEU8AAAA4GaKvIhPT0/XpEmT1LVrV0VERCgyMlK//PKL4uLiFBAQoG3bttnt365dO8XFxV2zHQAAACgpirSIt1qtevzxx3XLLbdoxYoVioqK0ogRI/T444/r7NmzKl26tCZOnKj09PQCj79WOwAAAFASFGkRv2PHDp08eVIjR46Up+elJeqbNWumt956S1arVVWrVtWDDz6oqVOnFnj8tdoBAACAkqBIi/hff/1VdevWlYeH/bDBwcGqXLmyJGncuHHaunVrvmkzea7VDgAAABR3RVrEe3h4yMvL66r7eHt76/XXX7/itJlrtQMAAADFXZEW8fXr19evv/4qY4zd9hkzZmjHjh22zy1btrzqtJlrtQMAAADFWZEW8U2aNJGfn59mz56t3NxcSdKWLVu0fPly1a5d227fvGkzZ86cKbCva7UDAAAAxVWRFvEWi0Vz5szRsWPH1LVrV4WHh+ujjz7SvHnz5OfnZ7dv3rSZ7OzsAvu6VjsAAABQXFnMX+e2lAB7PxirrNQkV4cBACgC94/5WAkJaa4Ow46/f8WbLiZ3RS4di3w6joeHRX5+3s7r32k9AwAAAHAKingAAADAzVDEAwAAAG6GIh4AAABwMxTxAAAAgJuhiAcAAADcjKerA3CFBsP5Ta8AUFLkZF10dQgA4HAlsohPSkqX1Vrilsd3CtaTdRxy6Vjk07HIJwDcXJhOAwAAALgZingAAADAzVDEAwAAAG6mRM6J9/PzdnUIxYq/f0VXh+A2LmZlKfUcL9kBAID/TYks4p9fOkmJ6cmuDgMl0MLB70miiAcAAP8bptMAAAAAboYiHgAAAHAzFPEAAACAm6GIBwAAANwMRTwAAADgZpxWxO/YsUORkZH5tq9Zs0Y9e/ZUt27dFB4ero8//liStGXLFkVERCgiIkKBgYHq0KGDIiIiNGLECElSQECAhgwZYtdXcnKy6tWrp1mzZjnrNAAAAICbTpEuMXn69GlNnTpVy5cvl6+vrzIyMhQZGam77rpL7du3V6tWrSRJkZGReuqppxQUFGR3/JEjR3T27FlVqlRJkrRu3Tr5+PgU5SkAAAAALlek02lSUlKUnZ2tzMxMSVKFChU0ZcoU1a5du1DHt2/fXt98843t85o1a9ShQwenxAoAAADcrIq0iK9bt67at2+vkJAQ9e7dW9OnT5fVatUdd9xRqOM7d+6stWvXSpISExMlSf7+/k6LFwAAALgZFfmLrZMmTdKGDRvUv39/xcfH66GHHtK6desKdWxgYKCOHDmitLQ0rVmzRqGhoU6OFgAAALj5FGkRHxsbq9WrV+vWW29Vr1699M9//lMvvfSSli1bVqjjLRaL2rZtq2+++UZr166liAcAAECJVKRFfNmyZfXOO+8oLi5OkmSM0f79+3XPPfcUuo/OnTvriy++UJkyZVS5cmVnhQoAAADctJy6Os2uXbsUGBho+xweHq6nnnpKw4cPV3Z2tiSpVatWtmUkC6NRo0ZKSEhQnz59HB4vAAAA4A6cVsQHBQVp//79Bbb16NHjqscuWrQo37bffvtN0qUpNRs3brRtf/rpp/+HKAEAAAD3w29sBQAAANwMRTwAAADgZijiAQAAADdDEQ8AAAC4GYp4AAAAwM1QxAMAAABuxqnrxN+s3u7ziqtDQAl1MSvL1SEAAIBioEQW8UlJ6bJajavDKBb8/SsqISHN1WEAAACUKEynAQAAANwMRTwAAADgZijiAQAAADdjMcYwORwAAABOl515UWfTSsYiDx4eFvn5eTut/xL5YuvGUc/rQmKSq8MAAAAoUcI+XSCVkCLe2ZhOAwAAALgZingAAADAzVDEAwAAAG6GIh4AAABwMxTxAAAAgJu5ahEfHx9/1T/XKy4uTvXr11dERIQiIiIUGhqq8ePHKzExUXFxcQoICNC2bdvsjmnXrp3i4uKu2Q4AAACUFFddYrJLly6yWCwyxigzM1Ply5eXp6enUlNT5efnp61bt173gFWrVlVUVJQkyRijGTNmaOTIkZo2bZpKly6tiRMnKjo6Wt7e+dfVvFY7AAAAUBJc9Un8Tz/9pB9//FHh4eF6++239cMPP2jHjh2aPXu2WrVq9T8PbrFY9PTTT+vQoUNKT09X1apV9eCDD2rq1KkF7n+tdgAAAKAkKNSc+H379qlLly62z+3bt9eBAwccEkCZMmV0xx13aMuWLZKkcePGaevWrfmmzeS5VjsAAABQ3BWqiLdardqxY4ft8+bNm2WxWBwWhMViUdmyZSVJ3t7eev311zVx4kSlp6fn2/da7QAAAEBxd9U58XleeuklPfvssypdurSsVqsk6f3333dIAFlZWTpy5IiCg4O1YMECSVLLli2vOm3mWu0AAABAcVaoIr5JkybauHGjDh48KIvFojp16sjTs1CHXpXVatWsWbN03333ycPD/ocC48aNU3h4uBISEgo89lrtAAAAQHFVqEr8/PnzmjZtmjZv3qycnBy1aNFCL7744g2tEHPmzBlFRERIulTE33PPPZoxY4bS0tLs9subNjNkyJAC+7lWOwAAAFBcWYwx5lo7TZw4Ubm5uYqMjFRubq6++OIL5ebmuu10lo2jnteFxCRXhwEAAFCihH26QAkJadfesRjw8LDIz895S6IX6kn87t27FR0dbfs8efJku9VqAAAAABSdQq1Ok5uba3uhVbo0DaZUqVJOCwoAAADAlRXqSXzz5s317LPPqn///pKkxYsXKygoyKmBAQAAAChYoYr4cePGac6cOZoxY4Zyc3PVqlUrPfnkk86ODQAAAEABClXEe3p6auTIkRo5cqSz4wEAAABwDYUq4tevX69p06YpOTlZly9m8+OPPzotMAAAAAAFK9QSkx07dtSYMWNUt25dWSwW2/bq1as7NTgAAAAUH9mZF3U2LcvVYRSJm2KJSW9vb4WEhDgtiKKWlJQuq/Wa311QCP7+FUvMeq/ORi4di3w6Fvl0LPLpOOTSscin+yjUEpMNGjRQTEyMs2MBAAAAUAhXfRIfGBgoi8Wi3Nxc/ec//1GZMmXk6ekpY4wsFgtz4gEAAAAXuGoRv2rVqiu2FWIqPQAAAAAnKNSLrY899pg+/vhju20PPfSQlixZ4rTAAAAAgP9F1sUcnUu94JKxXfpi68iRI3XkyBEdP35c4eHhtu05OTkqU6aM04Jytvenr9a5s+ddHQYAAACcaMIbvV0dgtNctYgfM2aMTpw4oYkTJ2rixIm27aVKlVLt2rWdHhwAAACA/K5axNeoUUM1atTQmjVrlJWVpT///FN16tRRZmamypUrV1QxAgAAALhMoZaY3Lt3r0JCQjRs2DCdPn1abdq0YWUaAAAAwEUKVcRPnTpVCxcuVKVKlVStWjVNmzZNb7zxhrNjAwAAAFCAQhXxmZmZdnPgg4ODlZub67SgAAAAAFxZoYp4T09PnTt3ThaLRZL0xx9/ODUoAAAAAFdWqCJ++PDhGjhwoE6ePKlRo0apf//+euKJJ25owDVr1qhnz57q1q2bwsPD9fHHH2vp0qUaMmRIvn3Hjx+vTz/9VMuXL1dAQEC+Xz61cOFCBQQEKC4u7oZiAQAAANzRVYv4s2fP6uzZs2rcuLHefPNNRUZG6u6779b777+voKCg6x7s9OnTmjp1qubPn6/o6Gj9+9//1urVq+Xr66uff/5ZSUlJtn0vXLigjRs32tanr1atmtauXWvX3/r16+Xj43PdcQAAAADu7KpLTDZr1sw2hUaSLv/lrhaLRfv377+uwVJSUpSdna3MzExJUoUKFTRlyhR5eXkpJCREq1evVmRkpCQpJiZGzZo1k6+vrySpadOm+uGHH3T+/HmVL19e8fHxqlChgipWrHhdMQAAAADu7qpP4rt3766aNWvqkUce0cqVK3XgwAHbn+st4CWpbt26at++vUJCQtS7d29Nnz5dVqtVd9xxh3r16mU3XWbFihXq3fv/f8uWp6enWrZsqU2bNkmSVq9erc6dO193DAAAAIC7u2oRP2XKFK1YsUJ169bVG2+8ob59++rzzz9XamrqDQ84adIkbdiwQf3791d8fLweeughrVu3Tk2bNlVKSoqOHz+uhIQEHT16VA8++KDdsZ07d7ZNqYmJiVFISMgNxwEAAAC4q6tOp5GkcuXKKSIiQhERETp16pSioqI0aNAg3XRGnkAAABV7SURBVHnnnXr33Xeva7DY2FidP39eYWFh6tWrl3r16qUlS5Zo2bJl6tixo7p3765Vq1apbNmyioiIkIeH/XeMoKAgTZw4UQcPHpSvry9TaQAAAFAiFWp1mjzJyclKTk5WSkqK0tLSrnuwsmXL6p133rGtJmOM0f79+3XPPfdIknr06KH169fbVrD5q1KlSqlFixZ6+eWXFRYWdt3jAwAAAMXBNZ/Enzx5UtHR0YqKilKpUqXUrVs3LVmyRLfeeut1D9asWTM99dRTGj58uLKzsyVJrVq10ogRIyRJf/vb3+Tr6yur1aoaNWoU2Efnzp0VFRWldu3aXff4AAAAQHFgMZcvOfMXkZGROnLkiMLCwtS9e3fde++9RRmb07w/fbXOnT3v6jAAAADgRBPe6K2EhOufPeIIHh4W+fl5O63/qz6J//777+Xl5aWlS5dq2bJltu3GGFksFv34449OCwwAAABAwa5axH/zzTdFFQcAAACAQrpqEV+9evWiigMAAABAIV3X6jQAAAAAXI8iHgAAAHAzV12dBgAAAHBXWRdzdC71gkvGdunqNMVVUlK6rFa+uziCv39Fly3dVNyQS8cin45FPh2LfDoOuXQs8uk+mE4DAAAAuBmKeAAAAMDNUMQDAAAAboYXWwEAAFCsZF28qHOpWS6NgRdbneDjqROUejbJ1WEAAADACUa99aEk1xbxzsZ0GgAAAMDNUMQDAAAAboYiHgAAAHAzFPEAAACAm6GIBwAAANwMRTwAAADgZm7KJSZ37Nih2bNna9GiRbZtcXFx6tSpk2rVqiVJslqtysjIUPfu3TVy5EhXhQoAAAAUuZuyiL+SqlWrKioqyvb59OnTCg0NVZcuXWzFPQAAAFDcufV0moSEBBljVKFCBVeHAgAAABQZt3oSf+bMGUVEROjixYtKSUlRgwYNNHv2bFWrVs3VoQEAAABFxq2exOdNp1m9erUiIiJkjFGLFi1cHRYAAABQpNyqiM/j4eGhMWPG6PTp05o/f76rwwEAAACKlFsW8ZLk6empMWPGaM6cOUpISHB1OAAAAECRuWnnxO/atUuBgYG2z40bN863T+vWrRUYGKj33ntPkydPLsrwAAAAAJe5KYv4oKAg7d+/v1D7fvLJJ06OBgAAALi5uO10GgAAAKCkoogHAAAA3AxFPAAAAOBmKOIBAAAAN0MRDwAAALgZizHGuDoIAAAAwFGyLl7UudQsl8bg4WGRn5+30/q/KZeYdLakpHRZrXx3cQR//4pKSEhzdRjFArl0LPLpWOTTscin45BLxyKf7oPpNAAAAICboYgHAAAA3AxFPAAAAOBmKOIBAAAAN8PqNAAAACi2crJylHLuQpGPy+o0TvDrou+UnZbp6jAAAADgZPc92cbVITgF02kAAAAAN0MRDwAAALgZingAAADAzVDEAwAAAG6GIh4AAABwMzfd6jQ5OTn66KOPFB0dLYvFotzcXPXo0UPDhg3T+PHj9d133+mWW26R1WpV2bJl9frrr6tu3bquDhsAAAAoMjddET9p0iQlJibqP//5j3x8fJSenq4RI0aoYsWKkqSRI0eqZ8+ekqSYmBi99NJLWrZsmStDBgAAAIrUTVXEnzp1StHR0dq8ebN8fHwkSd7e3nr55Zd1+PDhfPunpaWpSpUqRR0mAAAA4FI3VRG/Z88e1apVS7fccovd9lq1aqlWrVrauHGjZs6cqX/961+6cOGC4uPjNXfuXBdFCwAAALjGTVXES5LFYrH9fc2aNZo7d66sVqvKlCmju+++2246zY8//qjHHntMUVFRuv32210VMgAAAFCkbqrVaerXr6/ff/9d6enpkqROnTopKipKc+fOVUpKSr79GzdurJo1a+qXX34p6lABAAAAl7mpivjbbrtN3bp109ixY5Wamirp0mo1sbGx8vDIH+qJEycUFxfH6jQAAAAoUSzGGOPqIC5ntVq1YMECrVy5Urm5ucrIyFBQUJCGDh2qDz/80LbEZKlSpXTx4kU98sgj6tOnz3WN8eui75SdlumkMwAAAMDN4r4n2yghIa3Ix/XwsMjPz9tp/d90RXxRoIgHAAAoGYprEX9TTacBAAAAcG0U8QAAAICboYgHAAAA3AxFPAAAAOBmKOIBAAAAN1MiV6cBAABAyZCTlaOUcxeKfFxnr07j6bSeb2JJSemyWvnu4gj+/hVdsmxTcUQuHYt8Ohb5dCzy6Tjk0rHIp/tgOg0AAADgZijiAQAAADdDEQ8AAAC4GYp4AAAAwM2wOg0AAABKvKysbJ07l+mw/lidxgk++ugjpaamujoMAAAA3CRGjx4tyXFFvLMxnQYAAABwMxTxAAAAgJuhiAcAAADcDEU8AAAA4GYo4gEAAAA347IifseOHYqMjLTbNm7cOA0YMECXr3q5fPlyjRs3rlDtAAAAQElw0z2J3717tz799NMbbgcAAACKu5uiiP/Xv/6lyMhIXbhwQUOGDNHcuXP1559/FrjvtdoBAACA4s7lRfzy5cu1bt06ffDBBypXrpzuuOMODR8+XBMmTFBBv0z2Wu0AAABAcefSIv7gwYOaOHGiBg0apAoVKti2Dxo0SMaYK06buVY7AAAAUJy5tIivUKGCZs2apWnTpun8+fO27R4eHnrzzTevOG3mWu0AAABAcebSIr569epq166dHnjgAc2cOdOu7c4779Tw4cM1f/78Ao+9VjsAAABQXLl8TrwkjRkzRitXrtTZs2fttg8aNEgNGza84nHXagcAAACKI4spgW+HfvTRR0pNTXV1GAAAALhJjB49WgkJaQ7rz8PDIj8/b4f1l69/p/UMAAAAwCko4gEAAAA3QxEPAAAAuBmKeAAAAMDNUMQDAAAAboYiHgAAAHAzJXKJSQAAAOByWVnZOncu02H9OXuJSU+n9XwTS0nJkNXKdxdH8PPzVlJSuqvDKBbIpWORT8cin45FPh2HXDpWSc+nh4flpuyrIDyJBwAAANwMc+IBAAAAN0MRDwAAALgZingAAADAzVDEAwAAAG6GIh4AAABwMxTxAAAAgJuhiAcAAADcDEU8AAAA4GYo4gEAAAA345ZF/MqVKxUWFqaOHTvq888/z9e+adMmhYeHKzw8XKNHj1ZGRoYkKS4uTgMGDFBERIQiIyN14sQJSVJqaqqGDh2qzp07a8CAAUpISJAkZWVl6YUXXlDnzp3Vo0cP/f7770V3kkXE0bn8/fffbdv79u2r/fv3S5JOnDihwMBARUREKCIiQkOGDCm6kyxCjs7nzp07FRQUZMvb+PHjJZWMa1NyfD579uxpy2VoaKjuvfdeJSYmlojr80ZzuWfPHvXq1Uvh4eEaNmyY7f5Yku+bkuPzyb3Tsfnk3unYfJbke6ckpaenq2vXroqLi8vXtn//fvXs2VOhoaF68cUXlZOTI0mKj4/XgAED1KlTJz3xxBO2HDv03mnczKlTp0zbtm1NSkqKycjIMOHh4ebQoUO29nPnzplmzZrZts2bN8+8/vrrxhhjnn/+efP5558bY4z59NNPzejRo40xxkyaNMl8+OGHxhhjvvzyS/PMM88YY4z5+OOPzcSJE40xxuzcudP06dOnaE6yiDgjl/369TMbN240xhizfft2Ex4ebowxZs2aNbZcFlfOyOf8+fPNBx98kG+s4n5tGuOcfF7uhRdeMHPnzjXGFP/r80ZzabVaTXBwsPn222+NMcZ89dVXZtiwYcaYknvfNMY5+eTe6dh8cu90bD4vV5LuncYY8/PPP5uuXbuaevXqmePHj+dr79Kli/npp5+MMcaMHz/e9t+eoUOHmlWrVhljjJk9e7aZNm2aMcax9063exK/fft2NWvWTJUqVVL58uUVGhqqNWvW2NqPHj2q2267TbVr15YktW3bVjExMZIkq9Wq9PR0SdKFCxdUtmxZSVJsbKzCw8MlSV27dtXmzZuVnZ2t2NhYdevWTZLUtGlTJScnKz4+vsjO1dmckcs+ffqoVatWkqSAgACdPHlSkrR3714dPHhQERERGjRokH777bciO8+i4ox87t27V1u3blV4eLiGDx9uy2dxvzYl5+Qzz7fffqsDBw7o8ccfl1T8r88bzWVKSooyMzPVrFkz2/atW7cqKyurxN43Jefkk3unY/PJvdOx+cxT0u6dkrRkyRK98sorqlq1ar62EydOKDMzU40aNZJ06ScWa9asUXZ2tr7//nuFhobabZccW3O6XRF/5swZ+fv72z5XrVpVp0+ftn2+8847derUKR04cECS9PXXXysxMVGS9Mwzz2jhwoVq1aqVPvnkE9tFeHmfnp6e8vb2VnJycr6x/P39derUKaefY1FxRi579uypUqVKSZJmzpypkJAQSZKXl5e6deumL7/8UkOGDNGIESPsbgzFgTPyWbFiRUVGRmrlypUKDg7Wc889V+BYxe3alJyTzzwzZ87Uc889Z7tWi/v1eaO59PX1Vfny5bV161ZJ0ldffaXs7GylpKSU2Pum5Jx8cu90bD65dzo2n3lK2r1Tkt544w01adKkwLaCrqfTp08rJSVF3t7e8vT0tNv+12P+13un2xXxVqtVFovF9tkYY/fZx8dHU6dO1cSJE9WrVy9VrVpVpUuXliSNHTtWr732mrZs2aJJkybpqaeekjEm3xjGGHl4eOTrO297ceGsXBpjNHXqVO3evVsTJkyQJD399NP6xz/+IQ8PDwUHB6t8+fL6448/ivBsnc8Z+XzttdfUsWNHSVL//v11+PBhpaWlFftrU3Le9Xno0CGlpKSobdu2tr6K+/V5o7m0WCyaOXOmPvzwQ3Xv3l1paWmqVKmSLc+XKyn3Tcl5+eTeeYkj8sm90/HXZ0m8d17LlXL915xLyvf58mNu9N7pdldutWrVbC8BSFJCQoLdjzhyc3NVrVo1LV26VP/97391zz336Pbbb1dycrL++OMP29ON0NBQJSQkKCUlRVWrVrU9wcvJyVFGRoYqVaqkW2+9VWfOnLH1nZiYWOCPU9yVM3KZk5Oj559/Xnv37tWnn36qihUrSpIWLVpk923eGGP7hlpcODqfSUlJmjt3rnJzc+3GKVWqVLG/NiXnXJ+SFBMTo7CwMLuxivv1eaO5lC49KVq0aJFWrFihbt26yWq1qlKlSiX2vik5J5/cOx2bT+6djs2nVDLvndfy11znXU+VK1dWWlqa7Rq8/N/AkfdOtyviH3zwQX377bdKTk7WhQsXtG7dOrVu3drWbrFY9Oijj+r06dMyxmjhwoUKCwuTr6+vvLy8tGvXLknSDz/8oAoVKqhy5coKDg7WihUrJEmrV69WkyZNVLp0aQUHBysqKkqStGvXLnl5eem2224r+pN2EmfkcurUqUpPT9cnn3xi+4+QJH3//fdatmyZpEurBlitVv39738v2hN2Mkfns0qVKlq/fr3Wrl0rSVqxYoXuu+8+lS9fvthfm5Jzrk9J+vnnn/P9aLS4X583mktJmjBhgvbs2SNJWrBggTp16mR76lYS75uSc/LJvdOx+eTe6dh8SiXz3nkt1atXl5eXl3744QdJUlRUlFq3bq3SpUurSZMmWr16taRL12Dev4FD753XfPX1JhQdHW26dOliOnbsaObNm2eMMeaxxx4ze/bsMcYYs3HjRtO1a1fTsWNH88orr5isrCxjjDG7d+82vXv3Nl27djV9+/Y1v/zyizHGmJSUFDNs2DATFhZm+vbta3v7ODMz04wZM8aEhYWZ7t27m3379rngbJ3LkblMSkoy99xzj+nQoYPp1q2b7Y8xl96Wf+SRR0yXLl1Mz549zf79+11zwk7m6Gvz4MGDpm/fviYsLMwMHDjQxMfHG2NKxrVpjOPzaYwxnTt3NocPH7YbpyRcn/9LLrt3725CQ0PNyJEjTVpamjGmZN83jXFsPrl3Ov765N7p2HwaU3LvnXnatm1ru89dnsv9+/ebXr16mdDQUDNq1Chz8eJFY4wxcXFxZuDAgaZz587m0UcfNWfPnjXGOPbeaTGmgEnhAAAAAG5abjedBgAAACjpKOIBAAAAN0MRDwAAALgZingAAADAzVDEAwAAAG6GIh4AiqHs7Gy1bNlSjz32mKtDAQA4AUU8ABRD69evV926dbVv3z79/vvvrg4HAOBgrBMPAMVQZGSkwsLCdOjQIeXk5Oi1117T6NGjVa9ePT366KOSpC+++EI7d+7Uu+++qw0bNmju3LnKzs5W2bJlNXbsWAUGBmrWrFn6+eefdebMGQUEBGjcuHF6+eWXlZSUpISEBFWvXl3vvvuu/Pz8tGfPHr366qvKzs5WzZo1FR8fr3HjxikoKOiK/QMAbpCjf6MVAMC1Dh06ZOrVq2eSk5PN7t27TcOGDU1ycrL59ttvTdeuXW379e7d22zbts0cOXLEdO3a1SQnJxtjLv22yxYtWpiMjAwzc+ZMExoaarKzs40xxixcuNB8+OGHxhhjrFareeyxx8z8+fNNdna2ad26tYmNjTXGGPPtt9+agIAA89133121fwDAjfF09ZcIAIBjLV68WG3btpWvr698fX1Vo0YNLVmyREOHDtXFixe1d+9elStXTsnJyWrevLm++OILnTlzRo888oitD4vFomPHjkmSGjVqJE/PS/+5ePjhh7Vr1y4tWLBAR48e1aFDh3Tffffp4MGDkqTg4GBJUrNmzXT33XdLkrZt23bF/uvWrVsEGQGA4ociHgCKkfPnzysqKkplypRRu3btJEnp6en67LPP9Oijj6p3796KiopS6dKl1bt3b1ksFlmtVjVv3lzvvvuurZ+TJ0+qatWqWr9+vcqXL2/bPn36dO3Zs0e9evVSUFCQcnJyZIxRqVKlZP4yO7NUqVKSdNX+AQA3hhdbAaAYWblypSpVqqQtW7Zow4YN2rBhg2JiYnT+/HmtWbNGPXr00IYNG7R27Vr17NlTktS8eXNt27bN9gLspk2b1K1bN2VmZubrf+vWrXr44YfVvXt3+fn5afv27crNzVWtWrVUpkwZbd68WZK0Z88eHTx4UBaL5br6BwAUDk/iAaAYWbx4sQYPHmx7Ci5JPj4+ioyM1MKFCxUeHq57771XOTk5uvXWWyVJtWvX1muvvaZRo0bJGCNPT0/NnTtXFSpUyNf/iBEjNG3aNL333nsqXbq0GjdurGPHjsnT01OzZs3SK6+8ohkzZujOO+9UlSpVVLZs2evqHwBQOKxOAwBwiKlTp2rIkCGqUqWKTp48qYiICMXExMjHx8fVoQFAscOTeACAQ1SvXl2PPPKIPD09ZYzR5MmTKeABwEl4Eg8AAAC4GV5sBQAAANwMRTwAAADgZijiAQAAADdDEQ8AAAC4GYp4AAAAwM1QxAMAAABu5v8Ah/BzcW5eGFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "ax = sns.barplot(data=results, y='Method', x='Average')\n",
    "ax.set(xlim=(0.98,1))\n",
    "ax.set_title('Average of the Metrics', fontsize=18, loc='left')\n",
    "ax.set_xticklabel()\n",
    "ax = ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As observed, on average, kNN fared slightly better compared to GB, LR, SVM, and GRU, which, in turn, achieved very similar results. However, these methods performed nearly 99.9%, which is considered a good classification outcome. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Detection rate of Normal and Attack flow records**\n",
    "\n",
    "The following plot shows the results of each method for classifying normal and attack flow records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.catplot(data=results[['Method', 'Normal_Detect_Rate', 'Atk_Detect_Rate']], col='Method', col_wrap=3, kind='bar', height=3, aspect=2)\n",
    "ax.set(ylim=(0.97,1))\n",
    "ax.set_xticklabels(rotation=45)\n",
    "ax = ax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> kNN achieved the best classification outcomes. \n",
    "\n",
    "> GRU showed the most balanced approach regarding classifying normal and attack flows. \n",
    "\n",
    "> Although CNN achieved a relatively good classification of attacks, the classification of normal record flows was low compared to other methods. This result can explain the Accuracy rate of this method. This situation also occurs with the LSTM method, which achieved a good classification rate for normal flows and a low classification rate for attack ones. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}