Notebook-Network Intrusion Detection Rapport.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47d429b9-9df5-42df-8c12-382eabaaa738",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1866ea7a-8b8a-4a70-be15-625f67c71336",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "# Data manipulation and analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "# Machine learning\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Définir le chemin du dossier où enregistrer les modèles\n",
    "from joblib import dump\n",
    "joblib_dir = './joblib'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1d3a07-d145-4563-b861-2f62c226a4d9",
   "metadata": {},
   "source": [
    "### Data Loading and Initial Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab526b83-5e97-4384-80ea-0fc6d95ed761",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack</th>\n",
       "      <th>last_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>neptune</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>normal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>neptune</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type  service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           udp    other   SF        146          0     0   \n",
       "1         0           tcp  private   S0          0          0     0   \n",
       "2         0           tcp     http   SF        232       8153     0   \n",
       "3         0           tcp     http   SF        199        420     0   \n",
       "4         0           tcp  private  REJ          0          0     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
       "0               0       0    0  ...                    0.00   \n",
       "1               0       0    0  ...                    0.10   \n",
       "2               0       0    0  ...                    1.00   \n",
       "3               0       0    0  ...                    1.00   \n",
       "4               0       0    0  ...                    0.07   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                    0.60                         0.88   \n",
       "1                    0.05                         0.00   \n",
       "2                    0.00                         0.03   \n",
       "3                    0.00                         0.00   \n",
       "4                    0.07                         0.00   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                         0.00                  0.00   \n",
       "1                         0.00                  1.00   \n",
       "2                         0.04                  0.03   \n",
       "3                         0.00                  0.00   \n",
       "4                         0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                      0.00                   0.0                      0.00   \n",
       "1                      1.00                   0.0                      0.00   \n",
       "2                      0.01                   0.0                      0.01   \n",
       "3                      0.00                   0.0                      0.00   \n",
       "4                      0.00                   1.0                      1.00   \n",
       "\n",
       "    attack  last_flag  \n",
       "0   normal         15  \n",
       "1  neptune         19  \n",
       "2   normal         21  \n",
       "3   normal         21  \n",
       "4  neptune         21  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "train = pd.read_csv('Train.csv')\n",
    "test = pd.read_csv('Test.csv')\n",
    "\n",
    "# Define the column names\n",
    "columns = [\"duration\", \"protocol_type\", \"service\", \"flag\", \"src_bytes\", \"dst_bytes\", \"land\", \"wrong_fragment\", \"urgent\", \n",
    "           \"hot\", \"num_failed_logins\", \"logged_in\", \"num_compromised\", \"root_shell\", \"su_attempted\", \"num_root\", \n",
    "           \"num_file_creations\", \"num_shells\", \"num_access_files\", \"num_outbound_cmds\", \"is_host_login\", \n",
    "           \"is_guest_login\", \"count\", \"srv_count\", \"serror_rate\", \"srv_serror_rate\", \"rerror_rate\", \"srv_rerror_rate\", \n",
    "           \"same_srv_rate\", \"diff_srv_rate\", \"srv_diff_host_rate\", \"dst_host_count\", \"dst_host_srv_count\", \n",
    "           \"dst_host_same_srv_rate\", \"dst_host_diff_srv_rate\", \"dst_host_same_src_port_rate\", \n",
    "           \"dst_host_srv_diff_host_rate\", \"dst_host_serror_rate\", \"dst_host_srv_serror_rate\", \"dst_host_rerror_rate\", \n",
    "           \"dst_host_srv_rerror_rate\", \"attack\", \"last_flag\"]\n",
    "\n",
    "train.columns = columns\n",
    "test.columns = columns\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65c1b368-9479-47cc-a90d-90c3c07729bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125972 entries, 0 to 125971\n",
      "Data columns (total 43 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   duration                     125972 non-null  int64  \n",
      " 1   protocol_type                125972 non-null  object \n",
      " 2   service                      125972 non-null  object \n",
      " 3   flag                         125972 non-null  object \n",
      " 4   src_bytes                    125972 non-null  int64  \n",
      " 5   dst_bytes                    125972 non-null  int64  \n",
      " 6   land                         125972 non-null  int64  \n",
      " 7   wrong_fragment               125972 non-null  int64  \n",
      " 8   urgent                       125972 non-null  int64  \n",
      " 9   hot                          125972 non-null  int64  \n",
      " 10  num_failed_logins            125972 non-null  int64  \n",
      " 11  logged_in                    125972 non-null  int64  \n",
      " 12  num_compromised              125972 non-null  int64  \n",
      " 13  root_shell                   125972 non-null  int64  \n",
      " 14  su_attempted                 125972 non-null  int64  \n",
      " 15  num_root                     125972 non-null  int64  \n",
      " 16  num_file_creations           125972 non-null  int64  \n",
      " 17  num_shells                   125972 non-null  int64  \n",
      " 18  num_access_files             125972 non-null  int64  \n",
      " 19  num_outbound_cmds            125972 non-null  int64  \n",
      " 20  is_host_login                125972 non-null  int64  \n",
      " 21  is_guest_login               125972 non-null  int64  \n",
      " 22  count                        125972 non-null  int64  \n",
      " 23  srv_count                    125972 non-null  int64  \n",
      " 24  serror_rate                  125972 non-null  float64\n",
      " 25  srv_serror_rate              125972 non-null  float64\n",
      " 26  rerror_rate                  125972 non-null  float64\n",
      " 27  srv_rerror_rate              125972 non-null  float64\n",
      " 28  same_srv_rate                125972 non-null  float64\n",
      " 29  diff_srv_rate                125972 non-null  float64\n",
      " 30  srv_diff_host_rate           125972 non-null  float64\n",
      " 31  dst_host_count               125972 non-null  int64  \n",
      " 32  dst_host_srv_count           125972 non-null  int64  \n",
      " 33  dst_host_same_srv_rate       125972 non-null  float64\n",
      " 34  dst_host_diff_srv_rate       125972 non-null  float64\n",
      " 35  dst_host_same_src_port_rate  125972 non-null  float64\n",
      " 36  dst_host_srv_diff_host_rate  125972 non-null  float64\n",
      " 37  dst_host_serror_rate         125972 non-null  float64\n",
      " 38  dst_host_srv_serror_rate     125972 non-null  float64\n",
      " 39  dst_host_rerror_rate         125972 non-null  float64\n",
      " 40  dst_host_srv_rerror_rate     125972 non-null  float64\n",
      " 41  attack                       125972 non-null  object \n",
      " 42  last_flag                    125972 non-null  int64  \n",
      "dtypes: float64(15), int64(24), object(4)\n",
      "memory usage: 41.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "841e155a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=train, x=\"attack\", y=\"duration\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecc344bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=train, x=\"serror_rate\", y=\"rerror_rate\", hue=\"attack\")\n",
    "\n",
    "# Afficher la légende en dehors du graphique\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c29ccd4f-2713-4320-bf47-a4c41474cc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Protocol Type Distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='protocol_type', data=train)\n",
    "plt.title('Protocol Type Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "356928f6-7e2f-4d44-9b58-7ee25c977b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_filtered = train[train['attack'] != 'normal']\n",
    "\n",
    "# Créer le countplot avec les données filtrées\n",
    "plt.figure(figsize=(30, 8))\n",
    "sns.countplot(x='attack', data=train_filtered)\n",
    "plt.title('Distribution of Attack Classes')\n",
    "\n",
    "# Afficher la légende en dehors du graphique\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a397f542-5593-434f-97e2-52a8a516f0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_filtered = train[train['attack'] != 'normal']\n",
    "\n",
    "# Créer le countplot avec les données filtrées\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.countplot(data=train_filtered, x='protocol_type', hue='attack')\n",
    "plt.title('Protocol Type by Attack Type')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f567383c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attack_counts = train['attack'].value_counts()\n",
    "\n",
    "# Calculer la proportion des attaques nommées \"normal\" par rapport au nombre total d'attaques\n",
    "normal_count = attack_counts.get('normal', 0)\n",
    "total_count = attack_counts.sum()\n",
    "proportion_normal = normal_count / total_count\n",
    "\n",
    "# Créer un graphique en cercle pour représenter cette proportion\n",
    "labels = ['pas d attaque', 'Autres Attaques']\n",
    "sizes = [proportion_normal, 1 - proportion_normal]\n",
    "colors = ['#66b3ff', '#ff9999']\n",
    "explode = (0.1, 0)  # explode le premier secteur\n",
    "\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)\n",
    "plt.axis('equal')  # Le graphique est un cercle\n",
    "plt.title('Proportion des attaques normales par rapport à toutes les attaques')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93a60d44-d8d5-4a1f-a335-575b9f7c58d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Heatmap\n",
    "plt.figure(figsize=(14, 12))\n",
    "corr = train.select_dtypes(include=['float64', 'int64']).corr()\n",
    "sns.heatmap(corr, cmap='coolwarm', annot=False)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efb5c710",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9560fdd0-0c90-45e4-a9e4-e7d6dbec56d3",
   "metadata": {},
   "source": [
    "### Data Preprocessing and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d8a214d-c961-460e-b4de-724d1b191de0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in training data:\n",
      " duration                       0\n",
      "protocol_type                  0\n",
      "service                        0\n",
      "flag                           0\n",
      "src_bytes                      0\n",
      "dst_bytes                      0\n",
      "land                           0\n",
      "wrong_fragment                 0\n",
      "urgent                         0\n",
      "hot                            0\n",
      "num_failed_logins              0\n",
      "logged_in                      0\n",
      "num_compromised                0\n",
      "root_shell                     0\n",
      "su_attempted                   0\n",
      "num_root                       0\n",
      "num_file_creations             0\n",
      "num_shells                     0\n",
      "num_access_files               0\n",
      "num_outbound_cmds              0\n",
      "is_host_login                  0\n",
      "is_guest_login                 0\n",
      "count                          0\n",
      "srv_count                      0\n",
      "serror_rate                    0\n",
      "srv_serror_rate                0\n",
      "rerror_rate                    0\n",
      "srv_rerror_rate                0\n",
      "same_srv_rate                  0\n",
      "diff_srv_rate                  0\n",
      "srv_diff_host_rate             0\n",
      "dst_host_count                 0\n",
      "dst_host_srv_count             0\n",
      "dst_host_same_srv_rate         0\n",
      "dst_host_diff_srv_rate         0\n",
      "dst_host_same_src_port_rate    0\n",
      "dst_host_srv_diff_host_rate    0\n",
      "dst_host_serror_rate           0\n",
      "dst_host_srv_serror_rate       0\n",
      "dst_host_rerror_rate           0\n",
      "dst_host_srv_rerror_rate       0\n",
      "attack                         0\n",
      "last_flag                      0\n",
      "dtype: int64\n",
      "----------------------------------------------\n",
      "Missing values in test data:\n",
      " duration                       0\n",
      "protocol_type                  0\n",
      "service                        0\n",
      "flag                           0\n",
      "src_bytes                      0\n",
      "dst_bytes                      0\n",
      "land                           0\n",
      "wrong_fragment                 0\n",
      "urgent                         0\n",
      "hot                            0\n",
      "num_failed_logins              0\n",
      "logged_in                      0\n",
      "num_compromised                0\n",
      "root_shell                     0\n",
      "su_attempted                   0\n",
      "num_root                       0\n",
      "num_file_creations             0\n",
      "num_shells                     0\n",
      "num_access_files               0\n",
      "num_outbound_cmds              0\n",
      "is_host_login                  0\n",
      "is_guest_login                 0\n",
      "count                          0\n",
      "srv_count                      0\n",
      "serror_rate                    0\n",
      "srv_serror_rate                0\n",
      "rerror_rate                    0\n",
      "srv_rerror_rate                0\n",
      "same_srv_rate                  0\n",
      "diff_srv_rate                  0\n",
      "srv_diff_host_rate             0\n",
      "dst_host_count                 0\n",
      "dst_host_srv_count             0\n",
      "dst_host_same_srv_rate         0\n",
      "dst_host_diff_srv_rate         0\n",
      "dst_host_same_src_port_rate    0\n",
      "dst_host_srv_diff_host_rate    0\n",
      "dst_host_serror_rate           0\n",
      "dst_host_srv_serror_rate       0\n",
      "dst_host_rerror_rate           0\n",
      "dst_host_srv_rerror_rate       0\n",
      "attack                         0\n",
      "last_flag                      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## Check for missing values in both train and test datasets\n",
    "missing_values_train = train.isnull().sum()\n",
    "missing_values_test = test.isnull().sum()\n",
    "print(\"Missing values in training data:\\n\", missing_values_train)\n",
    "print(\"----------------------------------------------\")\n",
    "print(\"Missing values in test data:\\n\", missing_values_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfb5d80-9066-4e40-8050-e83457182767",
   "metadata": {},
   "source": [
    "#### Encoding categorical variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9480f2a8-c2b6-48b9-a5db-2f35a84f8420",
   "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack</th>\n",
       "      <th>last_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type  service  flag  src_bytes  dst_bytes  land  \\\n",
       "0         0              2       44     9        146          0     0   \n",
       "1         0              1       49     5          0          0     0   \n",
       "2         0              1       24     9        232       8153     0   \n",
       "3         0              1       24     9        199        420     0   \n",
       "4         0              1       49     1          0          0     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
       "0               0       0    0  ...                    0.00   \n",
       "1               0       0    0  ...                    0.10   \n",
       "2               0       0    0  ...                    1.00   \n",
       "3               0       0    0  ...                    1.00   \n",
       "4               0       0    0  ...                    0.07   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                    0.60                         0.88   \n",
       "1                    0.05                         0.00   \n",
       "2                    0.00                         0.03   \n",
       "3                    0.00                         0.00   \n",
       "4                    0.07                         0.00   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                         0.00                  0.00   \n",
       "1                         0.00                  1.00   \n",
       "2                         0.04                  0.03   \n",
       "3                         0.00                  0.00   \n",
       "4                         0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                      0.00                   0.0                      0.00   \n",
       "1                      1.00                   0.0                      0.00   \n",
       "2                      0.01                   0.0                      0.01   \n",
       "3                      0.00                   0.0                      0.00   \n",
       "4                      0.00                   1.0                      1.00   \n",
       "\n",
       "   attack  last_flag  \n",
       "0       0         15  \n",
       "1       1         19  \n",
       "2       0         21  \n",
       "3       0         21  \n",
       "4       1         21  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = ['protocol_type', 'service', 'flag']\n",
    "label_encoder = LabelEncoder()\n",
    "for col in categorical_cols:\n",
    "    train[col] = label_encoder.fit_transform(train[col])\n",
    "    test[col] = label_encoder.transform(test[col])\n",
    "\n",
    "# Handle the 'attack' feature by encoding it into numerical format\n",
    "train['attack'] = np.where(train['attack'] == 'normal', 0, 1)\n",
    "test['attack'] = np.where(test['attack'] == 'normal', 0, 1)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18be129f-c3b5-486f-af07-1a17b1f27773",
   "metadata": {},
   "source": [
    "#### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e5e7d71-651b-4a95-92cc-97b9fcc58a60",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
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       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
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       "      <td>-0.480192</td>\n",
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       "      <td>-0.376387</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.110249</td>\n",
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       "      <td>9</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>-0.002891</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>1.066389</td>\n",
       "      <td>-0.439077</td>\n",
       "      <td>-0.383105</td>\n",
       "      <td>0.066249</td>\n",
       "      <td>-0.572085</td>\n",
       "      <td>-0.602434</td>\n",
       "      <td>-0.387633</td>\n",
       "      <td>-0.345084</td>\n",
       "      <td>0</td>\n",
       "      <td>0.652820</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
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       "      <td>-0.004814</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
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       "      <td>...</td>\n",
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       "      <td>-0.439077</td>\n",
       "      <td>-0.480192</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.007762</td>\n",
       "      <td>-0.004919</td>\n",
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       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.005111</td>\n",
       "      <td>-0.068555</td>\n",
       "      <td>-0.480192</td>\n",
       "      <td>-0.289103</td>\n",
       "      <td>-0.639533</td>\n",
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       "      <td>0.652820</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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      "text/plain": [
       "   duration  protocol_type  service  flag  src_bytes  dst_bytes      land  \\\n",
       "0 -0.110249              2       44     9  -0.007737  -0.004919 -0.014089   \n",
       "1 -0.110249              1       49     5  -0.007762  -0.004919 -0.014089   \n",
       "2 -0.110249              1       24     9  -0.007723  -0.002891 -0.014089   \n",
       "3 -0.110249              1       24     9  -0.007728  -0.004814 -0.014089   \n",
       "4 -0.110249              1       49     1  -0.007762  -0.004919 -0.014089   \n",
       "\n",
       "   wrong_fragment    urgent       hot  ...  dst_host_same_srv_rate  \\\n",
       "0       -0.089486 -0.007736 -0.095076  ...               -1.161030   \n",
       "1       -0.089486 -0.007736 -0.095076  ...               -0.938288   \n",
       "2       -0.089486 -0.007736 -0.095076  ...                1.066389   \n",
       "3       -0.089486 -0.007736 -0.095076  ...                1.066389   \n",
       "4       -0.089486 -0.007736 -0.095076  ...               -1.005111   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                2.736829                     2.367719   \n",
       "1               -0.174418                    -0.480192   \n",
       "2               -0.439077                    -0.383105   \n",
       "3               -0.439077                    -0.480192   \n",
       "4               -0.068555                    -0.480192   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                    -0.289103             -0.639533   \n",
       "1                    -0.289103              1.608744   \n",
       "2                     0.066249             -0.572085   \n",
       "3                    -0.289103             -0.639533   \n",
       "4                    -0.289103             -0.639533   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                 -0.624872             -0.387633                 -0.376387   \n",
       "1                  1.618940             -0.387633                 -0.376387   \n",
       "2                 -0.602434             -0.387633                 -0.345084   \n",
       "3                 -0.624872             -0.387633                 -0.376387   \n",
       "4                 -0.624872              2.874386                  2.753890   \n",
       "\n",
       "   attack  last_flag  \n",
       "0       0  -1.965539  \n",
       "1       1  -0.219967  \n",
       "2       0   0.652820  \n",
       "3       0   0.652820  \n",
       "4       1   0.652820  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy the original dataframe to keep the original values for comparison\n",
    "train_original = train.copy()\n",
    "\n",
    "# Identifying Numerical Columns\n",
    "numerical_cols = [col for col in train.columns if col not in ['attack'] + categorical_cols]\n",
    "scaler = StandardScaler().fit(train[numerical_cols])  # Fit only on train data\n",
    "\n",
    "# Calculating means and standard deviations for scaling\n",
    "means = train[numerical_cols].mean()\n",
    "stds = train[numerical_cols].std()\n",
    "\n",
    "# Manual Scaling\n",
    "train[numerical_cols] = (train[numerical_cols] - means) / stds\n",
    "test[numerical_cols] = (test[numerical_cols] - means) / stds\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81540045-a3a5-44c8-925f-06cb8e35847d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125972 entries, 0 to 125971\n",
      "Data columns (total 42 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   duration                     125972 non-null  float64\n",
      " 1   protocol_type                125972 non-null  int32  \n",
      " 2   service                      125972 non-null  int32  \n",
      " 3   flag                         125972 non-null  int32  \n",
      " 4   src_bytes                    125972 non-null  float64\n",
      " 5   dst_bytes                    125972 non-null  float64\n",
      " 6   land                         125972 non-null  float64\n",
      " 7   wrong_fragment               125972 non-null  float64\n",
      " 8   urgent                       125972 non-null  float64\n",
      " 9   hot                          125972 non-null  float64\n",
      " 10  num_failed_logins            125972 non-null  float64\n",
      " 11  logged_in                    125972 non-null  float64\n",
      " 12  num_compromised              125972 non-null  float64\n",
      " 13  root_shell                   125972 non-null  float64\n",
      " 14  su_attempted                 125972 non-null  float64\n",
      " 15  num_root                     125972 non-null  float64\n",
      " 16  num_file_creations           125972 non-null  float64\n",
      " 17  num_shells                   125972 non-null  float64\n",
      " 18  num_access_files             125972 non-null  float64\n",
      " 19  is_host_login                125972 non-null  float64\n",
      " 20  is_guest_login               125972 non-null  float64\n",
      " 21  count                        125972 non-null  float64\n",
      " 22  srv_count                    125972 non-null  float64\n",
      " 23  serror_rate                  125972 non-null  float64\n",
      " 24  srv_serror_rate              125972 non-null  float64\n",
      " 25  rerror_rate                  125972 non-null  float64\n",
      " 26  srv_rerror_rate              125972 non-null  float64\n",
      " 27  same_srv_rate                125972 non-null  float64\n",
      " 28  diff_srv_rate                125972 non-null  float64\n",
      " 29  srv_diff_host_rate           125972 non-null  float64\n",
      " 30  dst_host_count               125972 non-null  float64\n",
      " 31  dst_host_srv_count           125972 non-null  float64\n",
      " 32  dst_host_same_srv_rate       125972 non-null  float64\n",
      " 33  dst_host_diff_srv_rate       125972 non-null  float64\n",
      " 34  dst_host_same_src_port_rate  125972 non-null  float64\n",
      " 35  dst_host_srv_diff_host_rate  125972 non-null  float64\n",
      " 36  dst_host_serror_rate         125972 non-null  float64\n",
      " 37  dst_host_srv_serror_rate     125972 non-null  float64\n",
      " 38  dst_host_rerror_rate         125972 non-null  float64\n",
      " 39  dst_host_srv_rerror_rate     125972 non-null  float64\n",
      " 40  attack                       125972 non-null  int32  \n",
      " 41  last_flag                    125972 non-null  float64\n",
      "dtypes: float64(38), int32(4)\n",
      "memory usage: 38.4 MB\n"
     ]
    },
    {
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       "        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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack</th>\n",
       "      <th>last_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.007737</td>\n",
       "      <td>-0.004919</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.161030</td>\n",
       "      <td>2.736829</td>\n",
       "      <td>2.367719</td>\n",
       "      <td>-0.289103</td>\n",
       "      <td>-0.639533</td>\n",
       "      <td>-0.624872</td>\n",
       "      <td>-0.387633</td>\n",
       "      <td>-0.376387</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.965539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.007762</td>\n",
       "      <td>-0.004919</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.938288</td>\n",
       "      <td>-0.174418</td>\n",
       "      <td>-0.480192</td>\n",
       "      <td>-0.289103</td>\n",
       "      <td>1.608744</td>\n",
       "      <td>1.618940</td>\n",
       "      <td>-0.387633</td>\n",
       "      <td>-0.376387</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.219967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>-0.002891</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>1.066389</td>\n",
       "      <td>-0.439077</td>\n",
       "      <td>-0.383105</td>\n",
       "      <td>0.066249</td>\n",
       "      <td>-0.572085</td>\n",
       "      <td>-0.602434</td>\n",
       "      <td>-0.387633</td>\n",
       "      <td>-0.345084</td>\n",
       "      <td>0</td>\n",
       "      <td>0.652820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.007728</td>\n",
       "      <td>-0.004814</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>1.066389</td>\n",
       "      <td>-0.439077</td>\n",
       "      <td>-0.480192</td>\n",
       "      <td>-0.289103</td>\n",
       "      <td>-0.639533</td>\n",
       "      <td>-0.624872</td>\n",
       "      <td>-0.387633</td>\n",
       "      <td>-0.376387</td>\n",
       "      <td>0</td>\n",
       "      <td>0.652820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.110249</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.007762</td>\n",
       "      <td>-0.004919</td>\n",
       "      <td>-0.014089</td>\n",
       "      <td>-0.089486</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>-0.095076</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.005111</td>\n",
       "      <td>-0.068555</td>\n",
       "      <td>-0.480192</td>\n",
       "      <td>-0.289103</td>\n",
       "      <td>-0.639533</td>\n",
       "      <td>-0.624872</td>\n",
       "      <td>2.874386</td>\n",
       "      <td>2.753890</td>\n",
       "      <td>1</td>\n",
       "      <td>0.652820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type  service  flag  src_bytes  dst_bytes      land  \\\n",
       "0 -0.110249              2       44     9  -0.007737  -0.004919 -0.014089   \n",
       "1 -0.110249              1       49     5  -0.007762  -0.004919 -0.014089   \n",
       "2 -0.110249              1       24     9  -0.007723  -0.002891 -0.014089   \n",
       "3 -0.110249              1       24     9  -0.007728  -0.004814 -0.014089   \n",
       "4 -0.110249              1       49     1  -0.007762  -0.004919 -0.014089   \n",
       "\n",
       "   wrong_fragment    urgent       hot  ...  dst_host_same_srv_rate  \\\n",
       "0       -0.089486 -0.007736 -0.095076  ...               -1.161030   \n",
       "1       -0.089486 -0.007736 -0.095076  ...               -0.938288   \n",
       "2       -0.089486 -0.007736 -0.095076  ...                1.066389   \n",
       "3       -0.089486 -0.007736 -0.095076  ...                1.066389   \n",
       "4       -0.089486 -0.007736 -0.095076  ...               -1.005111   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                2.736829                     2.367719   \n",
       "1               -0.174418                    -0.480192   \n",
       "2               -0.439077                    -0.383105   \n",
       "3               -0.439077                    -0.480192   \n",
       "4               -0.068555                    -0.480192   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                    -0.289103             -0.639533   \n",
       "1                    -0.289103              1.608744   \n",
       "2                     0.066249             -0.572085   \n",
       "3                    -0.289103             -0.639533   \n",
       "4                    -0.289103             -0.639533   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                 -0.624872             -0.387633                 -0.376387   \n",
       "1                  1.618940             -0.387633                 -0.376387   \n",
       "2                 -0.602434             -0.387633                 -0.345084   \n",
       "3                 -0.624872             -0.387633                 -0.376387   \n",
       "4                 -0.624872              2.874386                  2.753890   \n",
       "\n",
       "   attack  last_flag  \n",
       "0       0  -1.965539  \n",
       "1       1  -0.219967  \n",
       "2       0   0.652820  \n",
       "3       0   0.652820  \n",
       "4       1   0.652820  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.drop(columns=['num_outbound_cmds'])\n",
    "test = test.drop(columns=['num_outbound_cmds'])\n",
    "train.info()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8022c75-4bc8-4edf-b3aa-524e229a1925",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "feature = 'duration'\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(train_original[feature], bins=30, color='blue', kde=True)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of \"duration\" before Standard Scaling')  # Optional\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(train[feature], bins=30, color='orange', kde=True)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of \"duration\" after Standard Scaling')  # Optional\n",
    "\n",
    "plt.suptitle('Impact of Scaling on Feature Distribution')  # Optional main title\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da654f3d-c348-4a64-bfe2-0cc5291d1053",
   "metadata": {},
   "source": [
    "### Feature Engineering and Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d40aeac-f6ae-41f2-8342-92b72561bdba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['logged_in', 'flag', 'dst_host_same_srv_rate', 'same_srv_rate',\n",
      "       'dst_host_srv_count', 'dst_host_serror_rate', 'serror_rate',\n",
      "       'dst_host_srv_serror_rate', 'last_flag', 'protocol_type',\n",
      "       'srv_serror_rate'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "# Using ExtraTreesClassifier for Feature Selection\n",
    "model = ExtraTreesClassifier(n_estimators=100)\n",
    "X_train = train.drop('attack', axis=1)  # Dropping the 'attack' column for feature selection\n",
    "y_train = train['attack']\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Plotting Feature Importances\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X_train.columns)\n",
    "top_features = feat_importances.nlargest(11).index\n",
    "print(top_features)\n",
    "plt.figure(figsize=(20, 10))\n",
    "feat_importances.nlargest(11).plot(kind='barh')\n",
    "plt.title('Top 11 Feature Importances')\n",
    "plt.show()\n",
    "print(y_train.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f1ad46c-9636-4761-9238-7b1e93dde918",
   "metadata": {
    "scrolled": true
   },
   "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>logged_in</th>\n",
       "      <th>flag</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>last_flag</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>attack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.809264</td>\n",
       "      <td>9</td>\n",
       "      <td>-1.161030</td>\n",
       "      <td>-1.321415</td>\n",
       "      <td>-1.035689</td>\n",
       "      <td>-0.639533</td>\n",
       "      <td>-0.637210</td>\n",
       "      <td>-0.624872</td>\n",
       "      <td>-1.965539</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.631930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.809264</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.938288</td>\n",
       "      <td>-1.389655</td>\n",
       "      <td>-0.809859</td>\n",
       "      <td>1.608744</td>\n",
       "      <td>1.602649</td>\n",
       "      <td>1.618940</td>\n",
       "      <td>-0.219967</td>\n",
       "      <td>1</td>\n",
       "      <td>1.605089</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.235681</td>\n",
       "      <td>9</td>\n",
       "      <td>1.066389</td>\n",
       "      <td>0.771285</td>\n",
       "      <td>1.258741</td>\n",
       "      <td>-0.572085</td>\n",
       "      <td>-0.189238</td>\n",
       "      <td>-0.602434</td>\n",
       "      <td>0.652820</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.184526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.235681</td>\n",
       "      <td>9</td>\n",
       "      <td>1.066389</td>\n",
       "      <td>0.771285</td>\n",
       "      <td>1.258741</td>\n",
       "      <td>-0.639533</td>\n",
       "      <td>-0.637210</td>\n",
       "      <td>-0.624872</td>\n",
       "      <td>0.652820</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.631930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.809264</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.005111</td>\n",
       "      <td>-1.139441</td>\n",
       "      <td>-0.873091</td>\n",
       "      <td>-0.639533</td>\n",
       "      <td>-0.637210</td>\n",
       "      <td>-0.624872</td>\n",
       "      <td>0.652820</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.631930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   logged_in  flag  dst_host_same_srv_rate  same_srv_rate  dst_host_srv_count  \\\n",
       "0  -0.809264     9               -1.161030      -1.321415           -1.035689   \n",
       "1  -0.809264     5               -0.938288      -1.389655           -0.809859   \n",
       "2   1.235681     9                1.066389       0.771285            1.258741   \n",
       "3   1.235681     9                1.066389       0.771285            1.258741   \n",
       "4  -0.809264     1               -1.005111      -1.139441           -0.873091   \n",
       "\n",
       "   dst_host_serror_rate  serror_rate  dst_host_srv_serror_rate  last_flag  \\\n",
       "0             -0.639533    -0.637210                 -0.624872  -1.965539   \n",
       "1              1.608744     1.602649                  1.618940  -0.219967   \n",
       "2             -0.572085    -0.189238                 -0.602434   0.652820   \n",
       "3             -0.639533    -0.637210                 -0.624872   0.652820   \n",
       "4             -0.639533    -0.637210                 -0.624872   0.652820   \n",
       "\n",
       "   protocol_type  srv_serror_rate  attack  \n",
       "0              2        -0.631930       0  \n",
       "1              1         1.605089       1  \n",
       "2              1        -0.184526       0  \n",
       "3              1        -0.631930       0  \n",
       "4              1        -0.631930       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping top 15 features plus the target variable for both train and test datasets\n",
    "train = train[top_features.tolist() + ['attack']]\n",
    "test = test[top_features.tolist() + ['attack']]\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56c136e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 ... 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_test = test[top_features.tolist()].values\n",
    "y_test = test.attack.values\n",
    "X_train = train[top_features.tolist()].values\n",
    "y_train = train.attack.values\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edf7b4f-b1f9-4da9-ae0e-37a4ec97fca4",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d4061d0-07cf-4b5f-a4b1-79076a3c2c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.88812\n",
      "Random Forest Accuracy: 0.86022\n",
      "Naive Bayes Accuracy: 0.80260\n",
      "KNN Accuracy: 0.88999\n",
      "SVC Accuracy: 0.89735\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=1000, penalty='l2', C=0.01),  # Adjust C for the strength of regularization\n",
    "    \"Random Forest\": RandomForestClassifier(n_estimators=100),\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"KNN\": KNeighborsClassifier(),\n",
    "    \"SVC\": SVC()\n",
    "}\n",
    "results = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    results[name] = accuracy\n",
    "    print(f\"{name} Accuracy: {accuracy:.5f}\")\n",
    "    # Save each model to a file\n",
    "    model_path = os.path.join(joblib_dir, f'{name.replace(\" \", \"_\")}_model.joblib')\n",
    "    dump(model, model_path)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=list(results.keys()), y=list(results.values()))\n",
    "plt.title('Model Performance Comparison')\n",
    "plt.ylabel('Accuracy Score')\n",
    "plt.xlabel('Model')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb6f825a-0d5e-444a-b6ff-f8c1838fb1cf",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa683fe5-c434-4108-9bdf-ad9efbcf81c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model: SVC with accuracy 0.897351727809076\n"
     ]
    }
   ],
   "source": [
    "# Best Model Selection\n",
    "best_model_name = max(results, key=results.get)\n",
    "best_model = models[best_model_name]\n",
    "print(f\"Best Model: {best_model_name} with accuracy {results[best_model_name]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e64fedf1-98df-4097-9fb3-7f81ca4c0522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix of the best model\n",
    "y_pred = best_model.predict(X_test)  # Ensure X_test is your holdout test set\n",
    "conf_mat = confusion_matrix(y_test, y_pred)  # Ensure y_test is your holdout test set\n",
    "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(f'Confusion Matrix for {best_model_name}')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dde23e5-e549-4cd8-8eb5-76c9a4a8ed82",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### VotingClasifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0623c6a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Classifier Accuracy: 0.88431\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Définir les modèles\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(max_iter=1000, penalty='l2', C=0.01)),\n",
    "    (\"Random Forest\", RandomForestClassifier(n_estimators=100)),\n",
    "    (\"Naive Bayes\", GaussianNB()),\n",
    "    (\"KNN\", KNeighborsClassifier()),\n",
    "    (\"SVC\", SVC())\n",
    "]\n",
    "\n",
    "# Créer un VotingClassifier avec vote majoritaire\n",
    "voting_clf = VotingClassifier(estimators=models, voting='hard')\n",
    "\n",
    "# Entraîner le VotingClassifier sur les données d'entraînement\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Faire des prédictions sur les données de test\n",
    "y_pred_voting = voting_clf.predict(X_test)\n",
    "\n",
    "# Calculer l'accuracy du VotingClassifier\n",
    "accuracy_voting = accuracy_score(y_test, y_pred_voting)\n",
    "print(f\"Voting Classifier Accuracy: {accuracy_voting:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7fe78b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Classifier Accuracy: 0.89851\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Définir les modèles\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(max_iter=1000, penalty='l2', C=0.01)),\n",
    "    (\"KNN\", KNeighborsClassifier()),\n",
    "    (\"SVC\", SVC())\n",
    "]\n",
    "\n",
    "# Créer un VotingClassifier avec vote majoritaire\n",
    "voting_clf = VotingClassifier(estimators=models, voting='hard')\n",
    "\n",
    "# Entraîner le VotingClassifier sur les données d'entraînement\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Faire des prédictions sur les données de test\n",
    "y_pred_voting = voting_clf.predict(X_test)\n",
    "\n",
    "# Calculer l'accuracy du VotingClassifier\n",
    "accuracy_voting = accuracy_score(y_test, y_pred_voting)\n",
    "print(f\"Voting Classifier Accuracy: {accuracy_voting:.5f}\")\n"
   ]
  }
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