code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f3d7d69-34e3-4ef5-9497-ed17c088a5a6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_15772\\1048424940.py:5: DtypeWarning: Columns (9,11,12,13,14,15,16,18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  d=pd.read_csv(\"data_capec_multilabel.csv\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "http://test-site.com/                                           51185\n",
       "http://test-site.com/blog/index.php/page/2/                     24964\n",
       "http://test-site.com/blog/index.php/my-account/edit-profile/    19643\n",
       "http://test-site.com/blog/index.php/my-account/                 14526\n",
       "https://test-site.com/blog/index.php/my-account/                 9922\n",
       "Name: request_referer, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing columns names to similar ones as the dataset!\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d=pd.read_csv(\"data_capec_multilabel.csv\")\n",
    "\n",
    "d=d.rename(index=str, columns={'000 - Normal': 'normal'})\n",
    "d=d.rename(index=str, columns={'272 - Protocol Manipulation': 'protocol manipulation'})\n",
    "d=d.rename(index=str, columns={'242 - Code Injection': 'code injection'})\n",
    "d=d.rename(index=str, columns={'88 - OS Command Injection': 'os command injection'})\n",
    "d=d.rename(index=str, columns={'126 - Path Traversal': 'path traversal'})\n",
    "d=d.rename(index=str, columns={'66 - SQL Injection': 'sql injection'})\n",
    "d=d.rename(index=str, columns={'16 - Dictionary-based Password Attack': 'dictionary based password attack'})\n",
    "d=d.rename(index=str, columns={'310 - Scanning for Vulnerable Software': 'scanning for vulnerable software'})\n",
    "d=d.rename(index=str, columns={'153 - Input Data Manipulation': 'input data manipulation'})\n",
    "d=d.rename(index=str, columns={'248 - Command Injection': 'command injection'})\n",
    "d=d.rename(index=str, columns={'274 - HTTP Verb Tampering': 'http verb tampering'})\n",
    "d=d.rename(index=str, columns={'194 - Fake the Source of Data': 'fake the source of data'})\n",
    "d=d.rename(index=str, columns={'34 - HTTP Response Splitting': 'http response splitting'})\n",
    "d=d.rename(index=str, columns={'33 - HTTP Request Smuggling': 'http request smuggling'})\n",
    "d['request_referer'].value_counts().head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a3911e6-6021-41da-8fea-880e080fb151",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(907815, 38)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 907815 entries, 0 to 907814\n",
      "Data columns (total 38 columns):\n",
      " #   Column                            Non-Null Count   Dtype  \n",
      "---  ------                            --------------   -----  \n",
      " 0   timestamp                         907813 non-null  object \n",
      " 1   src_ip                            907813 non-null  object \n",
      " 2   src_port                          907813 non-null  float64\n",
      " 3   dst_ip                            907813 non-null  object \n",
      " 4   dst_port                          907813 non-null  float64\n",
      " 5   request_http_method               907813 non-null  object \n",
      " 6   request_http_request              907813 non-null  object \n",
      " 7   request_http_protocol             907813 non-null  object \n",
      " 8   request_user_agent                903981 non-null  object \n",
      " 9   request_referer                   291394 non-null  object \n",
      " 10  request_host                      907781 non-null  object \n",
      " 11  request_origin                    1609 non-null    object \n",
      " 12  request_cookie                    445262 non-null  object \n",
      " 13  request_content_type              28544 non-null   object \n",
      " 14  request_accept                    381783 non-null  object \n",
      " 15  request_accept_language           15860 non-null   object \n",
      " 16  request_accept_encoding           366300 non-null  object \n",
      " 17  request_do_not_track              705 non-null     float64\n",
      " 18  request_connection                482860 non-null  object \n",
      " 19  request_body                      26743 non-null   object \n",
      " 20  response_http_protocol            907813 non-null  object \n",
      " 21  response_http_status_code         907813 non-null  float64\n",
      " 22  response_http_status_message      907813 non-null  object \n",
      " 23  response_content_length           904523 non-null  float64\n",
      " 24  normal                            907815 non-null  int64  \n",
      " 25  protocol manipulation             907815 non-null  int64  \n",
      " 26  code injection                    907815 non-null  int64  \n",
      " 27  os command injection              907815 non-null  int64  \n",
      " 28  path traversal                    907815 non-null  int64  \n",
      " 29  sql injection                     907815 non-null  int64  \n",
      " 30  dictionary based password attack  907815 non-null  int64  \n",
      " 31  scanning for vulnerable software  907815 non-null  int64  \n",
      " 32  input data manipulation           907815 non-null  int64  \n",
      " 33  command injection                 907815 non-null  int64  \n",
      " 34  http verb tampering               907815 non-null  int64  \n",
      " 35  fake the source of data           907815 non-null  int64  \n",
      " 36  http response splitting           907815 non-null  int64  \n",
      " 37  http request smuggling            907815 non-null  int64  \n",
      "dtypes: float64(5), int64(14), object(19)\n",
      "memory usage: 270.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(d.shape)\n",
    "print(d.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5feb8ca-16e7-4d2e-9cc5-b206856f10e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d[['timestamp','src_port','request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length','normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbf18e9a-49d4-46c3-b0b8-038d445f115b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d.head(907814)\n",
    "d1='updated.csv'\n",
    "d.to_csv(d1,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "605404fd-d10c-4c47-9ced-4d45f5798d9d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                                0\n",
       "src_port                                 0\n",
       "request_http_method                      0\n",
       "request_http_request                     0\n",
       "request_referer                     616419\n",
       "request_user_agent                    3832\n",
       "request_cookie                      462551\n",
       "request_accept                      526030\n",
       "request_body                        881070\n",
       "response_http_status_message             0\n",
       "response_content_length                  0\n",
       "normal                                   0\n",
       "protocol manipulation                    0\n",
       "code injection                           0\n",
       "os command injection                     0\n",
       "path traversal                           0\n",
       "sql injection                            0\n",
       "dictionary based password attack         0\n",
       "scanning for vulnerable software         0\n",
       "input data manipulation                  0\n",
       "http verb tampering                      0\n",
       "fake the source of data                  0\n",
       "http response splitting                  0\n",
       "http request smuggling                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.isnull().sum()\n",
    "#d['request_http_method'].value_counts()\n",
    "\n",
    "#removing rows that are null \n",
    "d=d.dropna(subset=['timestamp'])\n",
    "d.isnull().sum()\n",
    "\n",
    "#making response content length =0 for those rows which have null value\n",
    "d['response_content_length'].fillna(0,inplace=True)\n",
    "d['response_content_length']=d['response_content_length'].astype(int)\n",
    "d.isnull().sum()\n",
    "#d['request_http_method'].value_counts()\n",
    "\n",
    "#d.to_csv(d2,index=False)\n",
    "d2=d[['timestamp','src_port','request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length','normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "d2.to_csv('new.csv',index=False)\n",
    "d.isnull().sum()\n",
    "#d2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7e490c5-c491-4278-8028-43a63f0e6837",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        timestamp  src_port request_http_method  \\\n",
      "13025  17/Jul/2020:21:39:19 +0200   64679.0                 GET   \n",
      "13030  17/Jul/2020:21:39:19 +0200   64679.0                 GET   \n",
      "13628  17/Jul/2020:21:40:12 +0200   64685.0                 GET   \n",
      "13629  17/Jul/2020:21:40:12 +0200   64686.0                 GET   \n",
      "13631  17/Jul/2020:21:40:12 +0200   64685.0                 GET   \n",
      "\n",
      "                                    request_http_request request_referer  \\\n",
      "13025  /blog/index.php/2020/04/04/labore-pariatur-ame...             NaN   \n",
      "13030  /blog/index.php/2020/04/04/labore-pariatur-ame...             NaN   \n",
      "13628  /blog/index.php/2020/04/04/nihil-tenetur-et-ad...             NaN   \n",
      "13629  /blog/index.php/2020/04/04/nihil-tenetur-et-ad...             NaN   \n",
      "13631  /blog/index.php/2020/04/04/nihil-tenetur-et-ad...             NaN   \n",
      "\n",
      "                                      request_user_agent  \\\n",
      "13025  Mozilla/5.0 (X11; Linux x86_64; rv:70.0) Gecko...   \n",
      "13030  Mozilla/5.0 (X11; Linux x86_64; rv:70.0) Gecko...   \n",
      "13628  Mozilla/5.0 (X11; Linux x86_64; rv:70.0) Gecko...   \n",
      "13629  Mozilla/5.0 (X11; Linux x86_64; rv:70.0) Gecko...   \n",
      "13631  Mozilla/5.0 (X11; Linux x86_64; rv:70.0) Gecko...   \n",
      "\n",
      "                              request_cookie request_accept request_body  \\\n",
      "13025  wordpress_test_cookie=WP+Cookie+check            NaN          NaN   \n",
      "13030  wordpress_test_cookie=WP+Cookie+check            NaN          NaN   \n",
      "13628  wordpress_test_cookie=WP+Cookie+check            NaN          NaN   \n",
      "13629  wordpress_test_cookie=WP+Cookie+check            NaN          NaN   \n",
      "13631  wordpress_test_cookie=WP+Cookie+check            NaN          NaN   \n",
      "\n",
      "      response_http_status_message  ...  os command injection  path traversal  \\\n",
      "13025                           OK  ...                     0               0   \n",
      "13030                           OK  ...                     0               0   \n",
      "13628                    Not Found  ...                     0               0   \n",
      "13629                    Not Found  ...                     0               1   \n",
      "13631                    Not Found  ...                     0               1   \n",
      "\n",
      "       sql injection  dictionary based password attack  \\\n",
      "13025              0                                 0   \n",
      "13030              0                                 0   \n",
      "13628              0                                 0   \n",
      "13629              0                                 0   \n",
      "13631              0                                 0   \n",
      "\n",
      "       scanning for vulnerable software  input data manipulation  \\\n",
      "13025                                 0                        0   \n",
      "13030                                 0                        0   \n",
      "13628                                 0                        0   \n",
      "13629                                 0                        0   \n",
      "13631                                 0                        0   \n",
      "\n",
      "       http verb tampering  fake the source of data  http response splitting  \\\n",
      "13025                    0                        0                        0   \n",
      "13030                    0                        0                        0   \n",
      "13628                    0                        0                        0   \n",
      "13629                    0                        0                        0   \n",
      "13631                    0                        0                        0   \n",
      "\n",
      "       http request smuggling  \n",
      "13025                       0  \n",
      "13030                       0  \n",
      "13628                       0  \n",
      "13629                       0  \n",
      "13631                       0  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "#Removing the duplicate rows\n",
    "d2.duplicated(subset=None).sum()\n",
    "exact_duplicates = d2[d2.duplicated(subset=None, keep=False)].head(5)\n",
    "print(exact_duplicates)\n",
    "#d2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd31139f-f45d-4e66-bd80-db517ff5266a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% values of response content length less than a particular choosen value\n",
      "38.89842952237961\n",
      "40.60285543388341\n",
      "52.17407109173365\n",
      "85.55407336092345\n",
      "85.78517822503092\n",
      "98.85538100908448\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#sns.countplot(d2['response_content_length'])\n",
    "#print(d2['response_content_length'].value_counts().sort_index())\n",
    "\n",
    "\n",
    "# Assuming d2 is your DataFrame\n",
    "top_colors = d2['response_content_length'].value_counts().nlargest(6)\n",
    "\n",
    "# Plot the pie chart\n",
    "plt.pie(top_colors, autopct='%.2f', labels=None)\n",
    "\n",
    "# Create a separate text representation for the top 6 colors\n",
    "for i, (label, count) in enumerate(zip(top_colors.index, top_colors)):\n",
    "    plt.text(1.1, 0.8 - i * 0.1, f\"{label}: {count}\", fontsize=10, color='black', ha='left')\n",
    "\n",
    "plt.legend(labels=d2['response_content_length'], loc='upper center', bbox_to_anchor=(0.5, -0.04), ncol=16)\n",
    "\n",
    "plt.title(\"response_content_length\")\n",
    "\n",
    "plt.show()\n",
    "print('% values of response content length less than a particular choosen value')\n",
    "print((d2['response_content_length']<277).mean()*100)\n",
    "print((d2['response_content_length']<400).mean()*100)\n",
    "print((d2['response_content_length']<25000).mean()*100)\n",
    "print((d2['response_content_length']<26000).mean()*100)\n",
    "print((d2['response_content_length']<50000).mean()*100)\n",
    "print((d2['response_content_length']<60000).mean()*100)\n",
    "\n",
    "#jiski content length 277 se kam h uska percentage dikha rha h\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "81340465-1975-4216-8c99-12744169c887",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_colors = d2['request_referer'].value_counts().nlargest(10)\n",
    "\n",
    "# Plot the pie chart\n",
    "plt.pie(top_colors, autopct='%.2f', labels=None)\n",
    "\n",
    "# Create a separate text representation for the top 6 colors\n",
    "for i, (label, count) in enumerate(zip(top_colors.index, top_colors)):\n",
    "    plt.text(1.1, 0.8 - i * 0.1, f\"{label}: {count}\", fontsize=10, color='black', ha='left')\n",
    "\n",
    "plt.legend(labels=d2['request_referer'], loc='upper center', bbox_to_anchor=(0.5, -0.04), ncol=16)\n",
    "\n",
    "\n",
    "plt.title(\"request_referer\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1a59139-a0f8-4b81-aca8-b1a0ae6e39e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d00860b8d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#top_colors = d2['request_http_method'].value_counts().nlargest(10)\n",
    "\n",
    "# Plot the pie chart\n",
    "plt.title(\"request_http_method\")\n",
    "plt.pie(d2['request_http_method'].value_counts())\n",
    "plt.legend(labels=d2['request_http_method'], loc='upper center', bbox_to_anchor=(0.5, -0.04), ncol=16)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1fc21fe0-fdbb-4a3a-b60c-925e6b3d5ffb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KDE plot for reponse_content_length\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.kdeplot(d2['response_content_length'], color='blue')\n",
    "plt.xlabel('X-axis label')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Kernel Density Estimate (KDE) Plot of Numerical Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0ecd3eb-fdcf-4785-99bd-5a73cb3e553a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.72414e+05, 3.06355e+05, 1.22993e+05, 3.09800e+03, 1.51400e+03,\n",
       "        3.79000e+02, 7.27000e+02, 2.20000e+02, 5.50000e+01, 5.80000e+01]),\n",
       " array([     0.,  23644.,  47288.,  70932.,  94576., 118220., 141864.,\n",
       "        165508., 189152., 212796., 236440.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hist\n",
    "plt.hist(d2['response_content_length'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c8c0ad-f953-48c9-980f-ebade59c17a8",
   "metadata": {},
   "source": [
    "Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38891f5b-7ca0-4617-b441-3b7b3dbf487a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd0b88f7-5228-40a5-820b-bc6717663551",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'src_port', 'request_http_method', 'request_http_request',\n",
       "       'request_referer', 'request_user_agent', 'request_cookie',\n",
       "       'request_accept', 'request_body', 'response_http_status_message',\n",
       "       'response_content_length', 'normal', 'protocol manipulation',\n",
       "       'code injection', 'os command injection', 'path traversal',\n",
       "       'sql injection', 'dictionary based password attack',\n",
       "       'scanning for vulnerable software', 'input data manipulation',\n",
       "       'http verb tampering', 'fake the source of data',\n",
       "       'http response splitting', 'http request smuggling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "609ed278-42ce-4c79-90bf-3e9dbf51ca33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GET          869268\n",
       "POST          28381\n",
       "TRACE          2340\n",
       "OPTIONS        1284\n",
       "HEAD           1103\n",
       "TRACK           906\n",
       "LOCK            453\n",
       "PROPPATCH       453\n",
       "PROPFIND        453\n",
       "MOVE            453\n",
       "MKCOL           453\n",
       "PATCH           453\n",
       "COPY            453\n",
       "PUT             453\n",
       "CONNECT         453\n",
       "UNLOCK          453\n",
       "THBQ              1\n",
       "Name: request_http_method, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2['request_http_method'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec266fc2-3e0f-41b9-9601-11feef94067b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/                                                                                     299009\n",
       "/blog/index.php/comments/feed/                                                         14271\n",
       "/blog/index.php/my-account/                                                             4440\n",
       "/blog/index.php/2020/04/04/ipsa-ea-porro-distinctio/                                    4389\n",
       "/blog/index.php/2020/04/04/hic-porro-nihil-non-rerum/                                   4265\n",
       "/blog/index.php/feed/                                                                   4156\n",
       "/blog/wp-login.php                                                                      3710\n",
       "/blog/index.php/registration/                                                           3460\n",
       "/blog/wp-login.php?action=register                                                      3355\n",
       "/blog/index.php/2020/04/04/explicabo-qui-fuga-distinctio-dolores-voluptatibus-sit/      3271\n",
       "Name: request_http_request, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2['request_http_request'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06c21357-bade-4e31-9855-1346a06c3631",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0 (X11; Linux x86_64; rv:70.0) Gecko/20100101 Firefox/70.0               506816\n",
       "Mozilla/5.0 (X11; Linux x86_64; rv:73.0) Gecko/20100101 Firefox/73.0                15483\n",
       "Mozilla/5.0 (compatible; Nmap Scripting Engine; https://nmap.org/book/nse.html)      2734\n",
       "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)              579\n",
       "Jakarta Commons-HttpClient/3.1                                                        453\n",
       "                                                                                    ...  \n",
       "HTTP://www.google.com/search?q=OWASP%20passion                                          1\n",
       "HTTP://www.google.com:80/search?q=OWASP%20information                                   1\n",
       "https://www.google.com/search?q=OWASP%20delivery                                        1\n",
       "https://www.google.com:80/search?q=OWASP%20science                                      1\n",
       "\\underscore.min.js                                                                      1\n",
       "Name: request_user_agent, Length: 2001, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2['request_user_agent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5d1691c-dbae-456b-af1d-78e328abcd59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         */*\n",
      "1         */*\n",
      "2         */*\n",
      "3         */*\n",
      "4         */*\n",
      "         ... \n",
      "907810    */*\n",
      "907811    */*\n",
      "907812    */*\n",
      "907813    */*\n",
      "907814    */*\n",
      "Name: request_accept, Length: 389012, dtype: object\n",
      "389012\n"
     ]
    }
   ],
   "source": [
    "#what are the non_null values in the 'request_accept'\n",
    "non_null_rows = d[d['request_accept'].notna()]#displaying all non null values\n",
    "d['request_accept']\n",
    "#count_encoding=d['request_http_method'].value_counts().to_dict()#doing frequency/count encoding\n",
    "#d['request_http_method']=d['request_http_method'].map(count_encoding)\n",
    "d['request_body'].value_counts()\n",
    "d.isnull().sum()\n",
    "\n",
    "filtered_d = d[(d['request_cookie'].isna()) & (d['normal'] == 1)]\n",
    "print(filtered_d['request_accept'])\n",
    "print(filtered_d.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebfa06f7-e3c7-4b21-88e3-62cd0d7f8ba8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         NaN\n",
      "1         NaN\n",
      "2         NaN\n",
      "3         NaN\n",
      "4         NaN\n",
      "         ... \n",
      "907810    NaN\n",
      "907811    NaN\n",
      "907812    NaN\n",
      "907813    NaN\n",
      "907814    NaN\n",
      "Name: request_cookie, Length: 389012, dtype: object\n",
      "389012\n"
     ]
    }
   ],
   "source": [
    "non_null_rows = d[d['request_cookie'].notna()]#displaying all non null values\n",
    "d['request_cookie']\n",
    "#count_encoding=d['request_http_method'].value_counts().to_dict()#doing frequency/count encoding\n",
    "#d['request_http_method']=d['request_http_method'].map(count_encoding)\n",
    "d['request_body'].value_counts()\n",
    "d.isnull().sum()\n",
    "\n",
    "filtered_d = d[(d['request_cookie'].isna()) & (d['normal'] == 1)]\n",
    "print(filtered_d['request_cookie'])\n",
    "print(filtered_d.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad8ccb29-815e-451b-a4fa-9768e13cedcb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                                0\n",
       "src_port                                 0\n",
       "request_http_method                      0\n",
       "request_http_request                     0\n",
       "request_referer                     616419\n",
       "request_user_agent                    3832\n",
       "request_cookie                      462551\n",
       "request_accept                      526030\n",
       "request_body                        881070\n",
       "response_http_status_message             0\n",
       "response_content_length                  0\n",
       "normal                                   0\n",
       "protocol manipulation                    0\n",
       "code injection                           0\n",
       "os command injection                     0\n",
       "path traversal                           0\n",
       "sql injection                            0\n",
       "dictionary based password attack         0\n",
       "scanning for vulnerable software         0\n",
       "input data manipulation                  0\n",
       "http verb tampering                      0\n",
       "fake the source of data                  0\n",
       "http response splitting                  0\n",
       "http request smuggling                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8567673-4e54-4236-8aaa-e2b817c0ccaa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "289406\n"
     ]
    }
   ],
   "source": [
    "#dropping non null values\n",
    "df_no_nulls = d2.dropna(subset=['request_referer'])\n",
    "\n",
    "# Calculate value counts for the specific column\n",
    "value_counts = df_no_nulls['request_referer'].value_counts()\n",
    "\n",
    "# Define the prefix you want to filter by\n",
    "prefix = 'http'\n",
    "\n",
    "# Filter rows that start with the specified prefix\n",
    "filtered_rows = df_no_nulls[df_no_nulls['request_referer'].str.startswith(prefix)]\n",
    "print(len(filtered_rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c16d98a1-8699-4755-8f35-ae55b3d82456",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_15772\\2298281467.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  d3[columns_to_encode]=encoded_df\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['request_http_method', 'request_http_request', 'request_referer',\n",
       "       'request_user_agent', 'request_cookie', 'request_accept',\n",
       "       'request_body', 'response_http_status_message',\n",
       "       'response_content_length', 'normal', 'protocol manipulation',\n",
       "       'code injection', 'os command injection', 'path traversal',\n",
       "       'sql injection', 'dictionary based password attack',\n",
       "       'scanning for vulnerable software', 'input data manipulation',\n",
       "       'http verb tampering', 'fake the source of data',\n",
       "       'http response splitting', 'http request smuggling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!pip install category_encoders\n",
    "import category_encoders as ce\n",
    "d3=d2[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length','normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "d3.to_csv('before_encoding.csv',index=False)\n",
    "\n",
    "columns_to_encode = ['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message']\n",
    "encoder = ce.CountEncoder(cols=columns_to_encode)\n",
    "encoded_df = encoder.fit_transform(d3[columns_to_encode])\n",
    "d3[columns_to_encode]=encoded_df\n",
    "d3.to_csv('encoded_file.csv',index=False)\n",
    "d4=pd.read_csv('encoded_file.csv')\n",
    "d4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0dcf0a0e-f9c7-4c12-9df6-159150ef054a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "request_http_method                 0\n",
       "request_http_request                0\n",
       "request_referer                     0\n",
       "request_user_agent                  0\n",
       "request_cookie                      0\n",
       "request_accept                      0\n",
       "request_body                        0\n",
       "response_http_status_message        0\n",
       "response_content_length             0\n",
       "normal                              0\n",
       "protocol manipulation               0\n",
       "code injection                      0\n",
       "os command injection                0\n",
       "path traversal                      0\n",
       "sql injection                       0\n",
       "dictionary based password attack    0\n",
       "scanning for vulnerable software    0\n",
       "input data manipulation             0\n",
       "http verb tampering                 0\n",
       "fake the source of data             0\n",
       "http response splitting             0\n",
       "http request smuggling              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f62bdb30-2678-4a7e-8fe1-6d7909fa3bb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8903ecc8-501f-4b75-83c2-1f193b8e32f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X = d4[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length']]\n",
    "\n",
    "# Targets (y)\n",
    "y = d4[['normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "rf_classifier = RandomForestClassifier(n_estimators=150, random_state=42)\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_classifier.predict(X_test)\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "# f1 = f1_score(y_test, y_pred, average='micro')\n",
    "# hamming = hamming_loss(y_test, y_pred)\n",
    "\n",
    "# print(\"Accuracy: \", accuracy)\n",
    "# print(\"F1 Score (Micro): \", f1)\n",
    "# print(\"Hamming Loss: \", hamming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d1eca3fa-fee7-4863-8e07-d6fc6a7acb13",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[22], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Evaluate the performance of the multi-label classification\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m accuracy_score, f1_score, hamming_loss\n\u001b[1;32m----> 3\u001b[0m accuracy \u001b[38;5;241m=\u001b[39m accuracy_score(y_test, y_pred)\n\u001b[0;32m      4\u001b[0m f1 \u001b[38;5;241m=\u001b[39m f1_score(y_test, y_pred, average\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmicro\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      5\u001b[0m hamming \u001b[38;5;241m=\u001b[39m hamming_loss(y_test, y_pred)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y_test' is not defined"
     ]
    }
   ],
   "source": [
    "# Evaluate the performance of the multi-label classification\n",
    "from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "hamming = hamming_loss(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"F1 Score (Micro): \", f1)\n",
    "print(\"Hamming Loss: \", hamming)\n",
    "f1_macro = f1_score(y_test, y_pred, average='macro')\n",
    "#print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred, average='macro')\n",
    "#print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "#print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Convert sparse predictions to dense numpy arrays \n",
    "#y_pred1_dense = y_pred.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)\n",
    "\n",
    "# Find the index corresponding to the 'normal' label\n",
    "label='sql injection'\n",
    "label_index = list(y_test.columns).index(label)\n",
    "\n",
    "# Calculate TP, TN, FP, FN for the 'normal' label\n",
    "TP = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 1))\n",
    "FN = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 0))\n",
    "TN = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 0))\n",
    "FP = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 1))\n",
    "\n",
    "# Calculate Sensitivity and Specificity\n",
    "sensitivity = TP / (TP + FN)\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "# Calculate Markedness\n",
    "informedness = sensitivity + specificity - 1\n",
    "\n",
    "#print(\"Sensitivity:\", sensitivity)\n",
    "#print(\"Specificity:\", specificity)\n",
    "print(\"Informedness:\", informedness)\n",
    "\n",
    "precision = TP / (TP + FP)\n",
    "negative_predictive_value = TN / (TN + FN)\n",
    "\n",
    "# Calculate Markedness\n",
    "markedness = precision + negative_predictive_value - 1\n",
    "\n",
    "#print(\"Precision:\", precision)\n",
    "#print(\"Negative Predictive Value:\", negative_predictive_value)\n",
    "print(\"Markedness:\", markedness)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c2927a-29dd-4c24-b640-6666dc1a3336",
   "metadata": {},
   "source": [
    "Binary Relevance+ Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa2b6167-be46-4ad5-9402-cf14b9263d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "\n",
    "X = d4[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length']]\n",
    "\n",
    "# Targets (y)\n",
    "y = d4[['normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a BinaryRelevance classifier with Random Forest as the base classifier\n",
    "classifier = BinaryRelevance(classifier=RandomForestClassifier(n_estimators=100, random_state=42), require_dense=[True, True])\n",
    "\n",
    "# Train the BinaryRelevance classifier\n",
    " classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the trained BinaryRelevance classifier\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbe3688a-f7f8-4fc5-9d87-dcc8ea1aa7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred1)\n",
    "f1 = f1_score(y_test, y_pred1, average='micro')\n",
    "hamming_loss_value = hamming_loss(y_test, y_pred1)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"F1-score (micro): {f1}\")\n",
    "print(f\"Hamming Loss: {hamming_loss_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26cae4d5-097f-4d06-ba37-fb406c2c120b",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_macro = f1_score(y_test, y_pred1, average='macro')\n",
    "print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred1, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred1, average='macro')\n",
    "print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred1, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred1, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Convert sparse predictions to dense numpy arrays \n",
    "y_pred1_dense = y_pred1.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred1_dense, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred1_dense, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffba145d-1773-4827-bd3b-009fe21a8699",
   "metadata": {},
   "source": [
    "Classifier Chain+Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99c992ec-1e55-4e1d-b04b-7448c610925c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.multioutput import ClassifierChain\n",
    "from sklearn.metrics import hamming_loss\n",
    "\n",
    "X = d4[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length']]\n",
    "\n",
    "# Targets (y)\n",
    "y = d4[['normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "base_classifier = RandomForestClassifier()\n",
    "classifier_chain = ClassifierChain(base_classifier, order='random')\n",
    "\n",
    "classifier_chain.fit(X_train, y_train)\n",
    "\n",
    "y_pred1 = classifier_chain.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred1)\n",
    "f1 = f1_score(y_test, y_pred1, average='micro')\n",
    "hamming_loss_value = hamming_loss(y_test, y_pred1)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"F1-score (micro): {f1}\")\n",
    "print(f\"Hamming Loss: {hamming_loss_value}\")\n",
    "#done here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "355973b3-f212-4059-90fa-65ee1a9731f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import joblib as jl\n",
    "# #running the cc_rf through pickle file\n",
    "\n",
    "# jl.dump(classifier_chain,'clas_chain_rf.pkl')\n",
    "\n",
    "# #import pandas as pd\n",
    "\n",
    "# #from sklearn.model_selection import train_test_split\n",
    "# from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "\n",
    "# loaded_c=jl.load('clas_chain_rf.pkl')\n",
    "# #d4=pd.read_csv('encoded_file.csv')\n",
    "# y_pred = loaded_c.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaae9bbc-7a35-4a7a-9ba0-d6520d644475",
   "metadata": {},
   "outputs": [],
   "source": [
    "#performance metrics for classifier_chain + random forest\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "hamming_loss_value = hamming_loss(y_test, y_pred)\n",
    "print('br+rf')\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"F1-score (micro): {f1}\")\n",
    "print(f\"Hamming Loss: {hamming_loss_value}\")\n",
    "\n",
    "f1_macro = f1_score(y_test, y_pred, average='macro')\n",
    "#print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred, average='macro')\n",
    "#print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "#print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# # Convert sparse predictions to dense numpy arrays \n",
    "# y_pred1_dense = y_pred.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)\n",
    "\n",
    "# Find the index corresponding to the 'normal' label\n",
    "label='sql injection'\n",
    "label_index = list(y_test.columns).index(label)\n",
    "\n",
    "# Calculate TP, TN, FP, FN for the 'normal' label\n",
    "TP = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 1))\n",
    "FN = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 0))\n",
    "TN = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 0))\n",
    "FP = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 1))\n",
    "\n",
    "# Calculate Sensitivity and Specificity\n",
    "sensitivity = TP / (TP + FN)\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "# Calculate Markedness\n",
    "informedness = sensitivity + specificity - 1\n",
    "\n",
    "# print(\"Sensitivity:\", sensitivity)\n",
    "# print(\"Specificity:\", specificity)\n",
    "print(\"Informedness:\", informedness)\n",
    "\n",
    "precision = TP / (TP + FP)\n",
    "negative_predictive_value = TN / (TN + FN)\n",
    "\n",
    "# Calculate Markedness\n",
    "markedness = precision + negative_predictive_value - 1\n",
    "\n",
    "#print(\"Precision:\", precision)\n",
    "#print(\"Negative Predictive Value:\", negative_predictive_value)\n",
    "print(\"Markedness:\", markedness)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72efe996-5992-43cf-8f01-c117b84fd813",
   "metadata": {},
   "source": [
    "\n",
    "LGBM+Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "501d11f5-21aa-4af8-9b3c-9803e4ed9ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "\n",
    "# Load your data and perform the train-test split (as you've done)\n",
    "\n",
    "X = d4[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length']]\n",
    "y = d4[['normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a BinaryRelevance classifier with LightGBM as the base classifier\n",
    "classifier_light = BinaryRelevance(classifier=LGBMClassifier(n_estimators=200, learning_rate=0.1), require_dense=[True, True])\n",
    "# Train the BinaryRelevance classifier\n",
    "classifier_light.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the trained BinaryRelevance classifier\n",
    "y_pred = classifier_light.predict(X_test)\n",
    "\n",
    "# Evaluate the performance of the multi-label classification\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "hamming = hamming_loss(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"F1 Score (Micro): \", f1)\n",
    "print(\"Hamming Loss: \", hamming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69183a6c-3474-4146-9a72-a7da9ec4dbce",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "hamming_loss_value = hamming_loss(y_test, y_pred)\n",
    "print('br+rf')\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"F1-score (micro): {f1}\")\n",
    "print(f\"Hamming Loss: {hamming_loss_value}\")\n",
    "\n",
    "f1_macro = f1_score(y_test, y_pred, average='macro')\n",
    "#print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred, average='macro')\n",
    "#print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "#print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# # Convert sparse predictions to dense numpy arrays \n",
    "# y_pred1_dense = y_pred.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)\n",
    "\n",
    "# Find the index corresponding to the 'normal' label\n",
    "label='sql injection'\n",
    "label_index = list(y_test.columns).index(label)\n",
    "\n",
    "# Calculate TP, TN, FP, FN for the 'normal' label\n",
    "TP = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 1))\n",
    "FN = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 0))\n",
    "TN = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 0))\n",
    "FP = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 1))\n",
    "\n",
    "# Calculate Sensitivity and Specificity\n",
    "sensitivity = TP / (TP + FN)\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "# Calculate Markedness\n",
    "informedness = sensitivity + specificity - 1\n",
    "\n",
    "# print(\"Sensitivity:\", sensitivity)\n",
    "# print(\"Specificity:\", specificity)\n",
    "print(\"Informedness:\", informedness)\n",
    "\n",
    "precision = TP / (TP + FP)\n",
    "negative_predictive_value = TN / (TN + FN)\n",
    "\n",
    "# Calculate Markedness\n",
    "markedness = precision + negative_predictive_value - 1\n",
    "\n",
    "#print(\"Precision:\", precision)\n",
    "#print(\"Negative Predictive Value:\", negative_predictive_value)\n",
    "print(\"Markedness:\", markedness)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d5574a-a98e-464d-92ce-b58a7be45b56",
   "metadata": {},
   "source": [
    "AdaBoost+Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e84eb81d-225e-4058-9bc1-cb187ba7d530",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "\n",
    "# Load your data and perform the train-test split (as you've done)\n",
    "X = d4[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length']]\n",
    "y = d4[['normal','protocol manipulation','code injection','os command injection','path traversal','sql injection','dictionary based password attack','scanning for vulnerable software','input data manipulation','http verb tampering','fake the source of data','http response splitting','http request smuggling']]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a BinaryRelevance classifier with AdaBoost as the base classifier\n",
    "base_classifier = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, random_state=42)\n",
    "classifier = BinaryRelevance(classifier=base_classifier, require_dense=[True, True])\n",
    "\n",
    "# Train the BinaryRelevance classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the trained BinaryRelevance classifier\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the performance of the multi-label classification\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "hamming = hamming_loss(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"F1 Score (Micro): \", f1)\n",
    "print(\"Hamming Loss: \", hamming)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae4dff2d-3538-49eb-aa4f-d017a6cacc71",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='micro')\n",
    "hamming_loss_value = hamming_loss(y_test, y_pred)\n",
    "print('br+rf')\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"F1-score (micro): {f1}\")\n",
    "print(f\"Hamming Loss: {hamming_loss_value}\")\n",
    "\n",
    "f1_macro = f1_score(y_test, y_pred, average='macro')\n",
    "#print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred, average='macro')\n",
    "#print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "#print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# # Convert sparse predictions to dense numpy arrays \n",
    "# y_pred1_dense = y_pred.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)\n",
    "\n",
    "# Find the index corresponding to the 'normal' label\n",
    "label='sql injection'\n",
    "label_index = list(y_test.columns).index(label)\n",
    "\n",
    "# Calculate TP, TN, FP, FN for the 'normal' label\n",
    "TP = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 1))\n",
    "FN = sum((y_test.iloc[:, label_index] == 1) & (y_pred[:, label_index] == 0))\n",
    "TN = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 0))\n",
    "FP = sum((y_test.iloc[:, label_index] == 0) & (y_pred[:, label_index] == 1))\n",
    "\n",
    "# Calculate Sensitivity and Specificity\n",
    "sensitivity = TP / (TP + FN)\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "# Calculate Markedness\n",
    "informedness = sensitivity + specificity - 1\n",
    "\n",
    "# print(\"Sensitivity:\", sensitivity)\n",
    "# print(\"Specificity:\", specificity)\n",
    "print(\"Informedness:\", informedness)\n",
    "\n",
    "precision = TP / (TP + FP)\n",
    "negative_predictive_value = TN / (TN + FN)\n",
    "\n",
    "# Calculate Markedness\n",
    "markedness = precision + negative_predictive_value - 1\n",
    "\n",
    "#print(\"Precision:\", precision)\n",
    "#print(\"Negative Predictive Value:\", negative_predictive_value)\n",
    "print(\"Markedness:\", markedness)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6f432e-c847-4bb7-9e39-4cb49b3801ce",
   "metadata": {},
   "source": [
    "Label_Powerset+Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d58ed378-59bb-4206-bfab-0043eb04def1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skmultilearn.problem_transform import LabelPowerset\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = d4[['request_http_method','request_http_request','request_referer','request_user_agent','request_cookie','request_accept','request_body','response_http_status_message','response_content_length']]\n",
    "y = d4[['normal','protocol manipulation','code injection','os command injection','path traversal',\n",
    "        'sql injection','dictionary based password attack','scanning for vulnerable software',\n",
    "        'input data manipulation','http verb tampering','fake the source of data',\n",
    "        'http response splitting','http request smuggling']]\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a LabelPowerset classifier with Random Forest as the base classifier\n",
    "rf_classifier = RandomForestClassifier(n_estimators=150, random_state=42)\n",
    "lp_classifier = LabelPowerset(classifier=rf_classifier)\n",
    "\n",
    "# Train the LabelPowerset classifier\n",
    "lp_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred11 = lp_classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45b3bedb-0ad7-4356-8a49-2e9ab8f74d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "accuracy = accuracy_score(y_test, y_pred11)\n",
    "f1 = f1_score(y_test, y_pred11, average='micro')\n",
    "hamming = hamming_loss(y_test, y_pred11)\n",
    "\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"F1 Score (Micro): \", f1)\n",
    "print(\"Hamming Loss: \", hamming)\n",
    "f1_macro = f1_score(y_test, y_pred11, average='macro')\n",
    "print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred11, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred11, average='macro')\n",
    "print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred11, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred11, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Convert sparse predictions to dense numpy arrays \n",
    "y_pred1_dense = y_pred11.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred1_dense, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred1_dense, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357639be-b71b-4cf1-8ab2-ad7581a6c63d",
   "metadata": {},
   "source": [
    "Multi_Output+Random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1623cf9e-0d22-4268-9e84-f53e33a667fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "X = d4[['request_http_method', 'request_http_request', 'request_referer', 'request_user_agent', 'request_cookie', 'request_accept', 'request_body', 'response_http_status_message', 'response_content_length']]\n",
    "y = d4[['normal', 'protocol manipulation', 'code injection', 'os command injection', 'path traversal', 'sql injection', 'dictionary based password attack', 'scanning for vulnerable software', 'input data manipulation', 'http verb tampering', 'fake the source of data', 'http response splitting', 'http request smuggling']]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a base Random Forest classifier\n",
    "base_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Create a MultiOutputClassifier with the Random Forest as the base classifier\n",
    "classifier = MultiOutputClassifier(base_classifier)\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred11= classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the performance\n",
    "from sklearn.metrics import accuracy_score, f1_score, hamming_loss\n",
    "accuracy = accuracy_score(y_test, y_pred11)\n",
    "f1 = f1_score(y_test, y_pred11, average='micro')\n",
    "hamming = hamming_loss(y_test, y_pred11)\n",
    "\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"F1 Score (Micro): \", f1)\n",
    "print(\"Hamming Loss: \", hamming)\n",
    "f1_macro = f1_score(y_test, y_pred11, average='macro')\n",
    "print('f1(macro) ',f1_macro)\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "precision_micro = precision_score(y_test, y_pred11, average='micro')\n",
    "print('precision micro ',precision_micro)\n",
    "\n",
    "precision_macro = precision_score(y_test, y_pred11, average='macro')\n",
    "print('precision macro ',precision_macro)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "recall_micro = recall_score(y_test, y_pred11, average='micro')\n",
    "recall_macro = recall_score(y_test, y_pred11, average='macro')\n",
    "print('recall micro ',recall_micro)\n",
    "print('recall_macro ',recall_macro)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Convert sparse predictions to dense numpy arrays \n",
    "y_pred1_dense = y_pred11.toarray()\n",
    "\n",
    "# Calculate micro-average ROC AUC\n",
    "micro_roc_auc = roc_auc_score(y_test, y_pred1_dense, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged ROC AUC:\", micro_roc_auc)\n",
    "\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "# Calculate micro-average Jaccard similarity\n",
    "micro_jaccard = jaccard_score(y_test, y_pred1_dense, average='micro')\n",
    "\n",
    "print(\"Micro-Averaged Jaccard Similarity:\", micro_jaccard)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e33912-73ef-4739-a4f7-1a13ad619dac",
   "metadata": {},
   "source": [
    "Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2867af20-01e7-4720-b86b-e23783b89fed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Algorithm names\n",
    "algorithms = [\n",
    "    \"Binary Relevance + RF\",\n",
    "    \"Binary Relevance + AdaBoost\",\n",
    "    \"Binary Relevance + LightGBM\",\n",
    "    \"Classifier Chain + RF\",\n",
    "    \"Labeled Power Set + RF\",\n",
    "    \"Multi-Output Classifier + RF\",\n",
    "    \"Random Forest\"\n",
    "]\n",
    "\n",
    "# Parameters\n",
    "parameters = [\n",
    "    \"Accuracy\",\n",
    "    \"F1-Micro\",\n",
    "    \"Precision Micro\",\n",
    "    \"Recall\",\n",
    "    \"ROC AUC\",\n",
    "    \"Jacquard Similarity\",\n",
    "    \"Informedness\",\n",
    "    \"Markedness\"\n",
    "]\n",
    "\n",
    "# Scores\n",
    "scores = [\n",
    "    [0.81246, 0.85553, 0.90286, 0.81292, 0.90277, 0.74754, 0.74953, 0.77892],\n",
    "    [0.80549, 0.85353,  0.90572, 0.80703, 0.89997, 0.74449, 0.74277, 0.77886],\n",
    "    [0.80321, 0.85442, 0.90981, 0.80539, 0.89932, 0.74584, 0.75663, 0.78360],\n",
    "    [0.85490, 0.85603,  0.85728, 0.85479, 0.92139, 0.74830, 0.80709, 0.72951],\n",
    "    [0.85450, 0.85571,  0.85681, 0.85462, 0.92128, 0.74781, 0.80572, 0.73010],\n",
    "    [0.81246    ,0.85553          ,0.90286   ,0.81292  ,0.92277   ,0.74754   ,0.78643  ,0.76010],\n",
    "    [0.81285    ,0.85552          ,0.90279   ,0.81295  ,0.90278   ,0.74751   ,0.74953  ,0.77892]\n",
    "]\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "line_styles = ['-', '--', '-.', ':', '-', '--', '-.']\n",
    "markers = ['o', 's', '^', 'D', 'v', '<', '>']\n",
    "\n",
    "for i in range(len(algorithms)):\n",
    "    plt.plot(parameters, scores[i], label=algorithms[i], linestyle=line_styles[i], marker=markers[i])\n",
    "\n",
    "plt.title('Metrics values by algorithm/model combination')\n",
    "plt.xlabel('Parameters')\n",
    "plt.ylabel('Scores')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3259f505-f8b2-4072-903f-e9247a79e495",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Data\n",
    "algorithms = [\n",
    "    \"Binary Relevance + LightGBM\",\n",
    "    \"Classifier Chain + Random Forest\",\n",
    "    \"Binary Relevance + AdaBoost\",\n",
    "    \"Binary Relevance + Random Forest\",\n",
    "    \"Labeled Power Set + Random Forest\",\n",
    "    \"Multi o/p Classifier + Random Forest\",\n",
    "    \"Random Forest\"\n",
    "]\n",
    "\n",
    "hamming_loss_values = [0.02135, 0.02236, 0.02154, 0.02135, 0.02242, 0.02135,0.02136]\n",
    "\n",
    "# Create a DataFrame\n",
    "data = {\"Algorithm\": algorithms, \"Hamming Loss\": hamming_loss_values}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(x=\"Algorithm\", y=\"Hamming Loss\", marker=\"o\", data=df)\n",
    "plt.title(\"Hamming Loss for Various Algorithms\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bdf994bd-7756-4758-a15a-70b1e3a46655",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from category_encoders import CountEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Assuming trf is your transformer\n",
    "trf = ColumnTransformer([\n",
    "    ('trf', CountEncoder(), ['request_http_method','request_http_request','request_referer','request_user_agent',\n",
    "                             'request_cookie','request_accept','request_body','response_http_status_message',\n",
    "                             'response_content_length'])\n",
    "])\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "\n",
    "# Create a two-step pipeline with Binary Relevance\n",
    "pipe = Pipeline(steps=[\n",
    "    ('step1', trf),\n",
    "    ('step2', BinaryRelevance(classifier=RandomForestClassifier(n_estimators=100, random_state=42)))\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03a3e948-20a0-4cc0-bd0c-883673d11bf7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;trf&#x27;,\n",
       "                                                  CountEncoder(combine_min_nan_groups=True),\n",
       "                                                  [&#x27;request_http_method&#x27;,\n",
       "                                                   &#x27;request_http_request&#x27;,\n",
       "                                                   &#x27;request_referer&#x27;,\n",
       "                                                   &#x27;request_user_agent&#x27;,\n",
       "                                                   &#x27;request_cookie&#x27;,\n",
       "                                                   &#x27;request_accept&#x27;,\n",
       "                                                   &#x27;request_body&#x27;,\n",
       "                                                   &#x27;response_http_status_message&#x27;,\n",
       "                                                   &#x27;response_content_length&#x27;])])),\n",
       "                (&#x27;step2&#x27;,\n",
       "                 BinaryRelevance(classifier=RandomForestClassifier(random_state=42),\n",
       "                                 require_dense=[True, True]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;trf&#x27;,\n",
       "                                                  CountEncoder(combine_min_nan_groups=True),\n",
       "                                                  [&#x27;request_http_method&#x27;,\n",
       "                                                   &#x27;request_http_request&#x27;,\n",
       "                                                   &#x27;request_referer&#x27;,\n",
       "                                                   &#x27;request_user_agent&#x27;,\n",
       "                                                   &#x27;request_cookie&#x27;,\n",
       "                                                   &#x27;request_accept&#x27;,\n",
       "                                                   &#x27;request_body&#x27;,\n",
       "                                                   &#x27;response_http_status_message&#x27;,\n",
       "                                                   &#x27;response_content_length&#x27;])])),\n",
       "                (&#x27;step2&#x27;,\n",
       "                 BinaryRelevance(classifier=RandomForestClassifier(random_state=42),\n",
       "                                 require_dense=[True, True]))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">step1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;trf&#x27;,\n",
       "                                 CountEncoder(combine_min_nan_groups=True),\n",
       "                                 [&#x27;request_http_method&#x27;, &#x27;request_http_request&#x27;,\n",
       "                                  &#x27;request_referer&#x27;, &#x27;request_user_agent&#x27;,\n",
       "                                  &#x27;request_cookie&#x27;, &#x27;request_accept&#x27;,\n",
       "                                  &#x27;request_body&#x27;,\n",
       "                                  &#x27;response_http_status_message&#x27;,\n",
       "                                  &#x27;response_content_length&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trf</label><div class=\"sk-toggleable__content\"><pre>[&#x27;request_http_method&#x27;, &#x27;request_http_request&#x27;, &#x27;request_referer&#x27;, &#x27;request_user_agent&#x27;, &#x27;request_cookie&#x27;, &#x27;request_accept&#x27;, &#x27;request_body&#x27;, &#x27;response_http_status_message&#x27;, &#x27;response_content_length&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountEncoder</label><div class=\"sk-toggleable__content\"><pre>CountEncoder(combine_min_nan_groups=True)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">step2: BinaryRelevance</label><div class=\"sk-toggleable__content\"><pre>BinaryRelevance(classifier=RandomForestClassifier(random_state=42),\n",
       "                require_dense=[True, True])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">classifier: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(transformers=[('trf',\n",
       "                                                  CountEncoder(combine_min_nan_groups=True),\n",
       "                                                  ['request_http_method',\n",
       "                                                   'request_http_request',\n",
       "                                                   'request_referer',\n",
       "                                                   'request_user_agent',\n",
       "                                                   'request_cookie',\n",
       "                                                   'request_accept',\n",
       "                                                   'request_body',\n",
       "                                                   'response_http_status_message',\n",
       "                                                   'response_content_length'])])),\n",
       "                ('step2',\n",
       "                 BinaryRelevance(classifier=RandomForestClassifier(random_state=42),\n",
       "                                 require_dense=[True, True]))])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e018b3f9-7b53-405d-8645-6284540c710d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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