{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36a841f4-893b-4d67-94a6-7b1f8ffaf819",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eac0e37a-2e9a-421d-b781-fe52007fd240",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.sparse import hstack, csr_matrix\n",
    "\n",
    "from sklearn.model_selection import (\n",
    "    train_test_split,\n",
    "    StratifiedKFold,\n",
    "    GridSearchCV\n",
    ")\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    roc_auc_score,\n",
    "    confusion_matrix,\n",
    "    classification_report,\n",
    "    roc_curve,\n",
    "    auc\n",
    ")\n",
    "\n",
    "from sklearn.base import clone\n",
    "\n",
    "RANDOM_STATE = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66537846-9aea-4487-b789-81ce48f5f387",
   "metadata": {},
   "source": [
    "# Load the CSIC 2010 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6089e9f8-aa87-40b7-a834-2c69af963115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (61065, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Method</th>\n",
       "      <th>User-Agent</th>\n",
       "      <th>Pragma</th>\n",
       "      <th>Cache-Control</th>\n",
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       "      <th>Accept-charset</th>\n",
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       "      <th>host</th>\n",
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       "      <th>content-type</th>\n",
       "      <th>connection</th>\n",
       "      <th>lenght</th>\n",
       "      <th>content</th>\n",
       "      <th>classification</th>\n",
       "      <th>URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>close</td>\n",
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       "      <td>0</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/anadir.j...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Normal</td>\n",
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       "      <td>no-cache</td>\n",
       "      <td>no-cache</td>\n",
       "      <td>text/xml,application/xml,application/xhtml+xml...</td>\n",
       "      <td>x-gzip, x-deflate, gzip, deflate</td>\n",
       "      <td>utf-8, utf-8;q=0.5, *;q=0.5</td>\n",
       "      <td>en</td>\n",
       "      <td>localhost:8080</td>\n",
       "      <td>JSESSIONID=933185092E0B668B90676E0A2B0767AF</td>\n",
       "      <td>application/x-www-form-urlencoded</td>\n",
       "      <td>Connection: close</td>\n",
       "      <td>Content-Length: 68</td>\n",
       "      <td>id=3&amp;nombre=Vino+Rioja&amp;precio=100&amp;cantidad=55&amp;...</td>\n",
       "      <td>0</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/anadir.j...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Normal</td>\n",
       "      <td>GET</td>\n",
       "      <td>Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...</td>\n",
       "      <td>no-cache</td>\n",
       "      <td>no-cache</td>\n",
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       "      <td>utf-8, utf-8;q=0.5, *;q=0.5</td>\n",
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       "      <td>localhost:8080</td>\n",
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       "      <td>0</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/autentic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Normal</td>\n",
       "      <td>POST</td>\n",
       "      <td>Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...</td>\n",
       "      <td>no-cache</td>\n",
       "      <td>no-cache</td>\n",
       "      <td>text/xml,application/xml,application/xhtml+xml...</td>\n",
       "      <td>x-gzip, x-deflate, gzip, deflate</td>\n",
       "      <td>utf-8, utf-8;q=0.5, *;q=0.5</td>\n",
       "      <td>en</td>\n",
       "      <td>localhost:8080</td>\n",
       "      <td>JSESSIONID=7104E6C68A6BCF1423DAE990CE49FEE2</td>\n",
       "      <td>application/x-www-form-urlencoded</td>\n",
       "      <td>Connection: close</td>\n",
       "      <td>Content-Length: 63</td>\n",
       "      <td>modo=entrar&amp;login=choong&amp;pwd=d1se3ci%F3n&amp;remem...</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "  Unnamed: 0 Method                                         User-Agent  \\\n",
       "0     Normal    GET  Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...   \n",
       "1     Normal    GET  Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...   \n",
       "2     Normal   POST  Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...   \n",
       "3     Normal    GET  Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...   \n",
       "4     Normal   POST  Mozilla/5.0 (compatible; Konqueror/3.5; Linux)...   \n",
       "\n",
       "     Pragma Cache-Control                                             Accept  \\\n",
       "0  no-cache      no-cache  text/xml,application/xml,application/xhtml+xml...   \n",
       "1  no-cache      no-cache  text/xml,application/xml,application/xhtml+xml...   \n",
       "2  no-cache      no-cache  text/xml,application/xml,application/xhtml+xml...   \n",
       "3  no-cache      no-cache  text/xml,application/xml,application/xhtml+xml...   \n",
       "4  no-cache      no-cache  text/xml,application/xml,application/xhtml+xml...   \n",
       "\n",
       "                    Accept-encoding               Accept-charset language  \\\n",
       "0  x-gzip, x-deflate, gzip, deflate  utf-8, utf-8;q=0.5, *;q=0.5       en   \n",
       "1  x-gzip, x-deflate, gzip, deflate  utf-8, utf-8;q=0.5, *;q=0.5       en   \n",
       "2  x-gzip, x-deflate, gzip, deflate  utf-8, utf-8;q=0.5, *;q=0.5       en   \n",
       "3  x-gzip, x-deflate, gzip, deflate  utf-8, utf-8;q=0.5, *;q=0.5       en   \n",
       "4  x-gzip, x-deflate, gzip, deflate  utf-8, utf-8;q=0.5, *;q=0.5       en   \n",
       "\n",
       "             host                                       cookie  \\\n",
       "0  localhost:8080  JSESSIONID=1F767F17239C9B670A39E9B10C3825F4   \n",
       "1  localhost:8080  JSESSIONID=81761ACA043B0E6014CA42A4BCD06AB5   \n",
       "2  localhost:8080  JSESSIONID=933185092E0B668B90676E0A2B0767AF   \n",
       "3  localhost:8080  JSESSIONID=8FA18BA82C5336D03D3A8AFA3E68CBB0   \n",
       "4  localhost:8080  JSESSIONID=7104E6C68A6BCF1423DAE990CE49FEE2   \n",
       "\n",
       "                        content-type         connection              lenght  \\\n",
       "0                                NaN              close                 NaN   \n",
       "1                                NaN              close                 NaN   \n",
       "2  application/x-www-form-urlencoded  Connection: close  Content-Length: 68   \n",
       "3                                NaN              close                 NaN   \n",
       "4  application/x-www-form-urlencoded  Connection: close  Content-Length: 63   \n",
       "\n",
       "                                             content  classification  \\\n",
       "0                                                NaN               0   \n",
       "1                                                NaN               0   \n",
       "2  id=3&nombre=Vino+Rioja&precio=100&cantidad=55&...               0   \n",
       "3                                                NaN               0   \n",
       "4  modo=entrar&login=choong&pwd=d1se3ci%F3n&remem...               0   \n",
       "\n",
       "                                                 URL  \n",
       "0   http://localhost:8080/tienda1/index.jsp HTTP/1.1  \n",
       "1  http://localhost:8080/tienda1/publico/anadir.j...  \n",
       "2  http://localhost:8080/tienda1/publico/anadir.j...  \n",
       "3  http://localhost:8080/tienda1/publico/autentic...  \n",
       "4  http://localhost:8080/tienda1/publico/autentic...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FILE_PATH = \"csic_database.csv\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    FILE_PATH,\n",
    "    encoding=\"utf-8\",\n",
    "    low_memory=False\n",
    ")\n",
    "\n",
    "print(\"Dataset shape:\", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612618f0-e76b-403f-98dc-af38e47fc15a",
   "metadata": {},
   "source": [
    "# Inspect dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09e61a27-d78c-4e97-a59a-3fcb8c5a656c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 61065\n",
      "Columns: 17\n"
     ]
    }
   ],
   "source": [
    "# Rows and Columns \n",
    "print(\"Rows:\", df.shape[0])\n",
    "print(\"Columns:\", df.shape[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3ea4d4f-1ae2-424a-8913-8643c7a3ac72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Column names:\n",
      "['Unnamed: 0', 'Method', 'User-Agent', 'Pragma', 'Cache-Control', 'Accept', 'Accept-encoding', 'Accept-charset', 'language', 'host', 'cookie', 'content-type', 'connection', 'lenght', 'content', 'classification', 'URL']\n",
      "\n",
      "Data types:\n",
      "Unnamed: 0         object\n",
      "Method             object\n",
      "User-Agent         object\n",
      "Pragma             object\n",
      "Cache-Control      object\n",
      "Accept             object\n",
      "Accept-encoding    object\n",
      "Accept-charset     object\n",
      "language           object\n",
      "host               object\n",
      "cookie             object\n",
      "content-type       object\n",
      "connection         object\n",
      "lenght             object\n",
      "content            object\n",
      "classification      int64\n",
      "URL                object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Column name and Data types\n",
    "print(\"\\nColumn names:\")\n",
    "print(df.columns.tolist())\n",
    "\n",
    "print(\"\\nData types:\")\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "500b5135-e6f2-42a7-965a-1de95384f423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing values:\n",
      "Unnamed: 0             0\n",
      "Method                 0\n",
      "User-Agent             0\n",
      "Pragma                 0\n",
      "Cache-Control          0\n",
      "Accept               397\n",
      "Accept-encoding        0\n",
      "Accept-charset         0\n",
      "language               0\n",
      "host                   0\n",
      "cookie                 0\n",
      "content-type       43088\n",
      "connection             0\n",
      "lenght             43088\n",
      "content            43088\n",
      "classification         0\n",
      "URL                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Missing values\n",
    "print(\"\\nMissing values:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa113039-e41e-46f3-921b-6f20ae77a301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "# Duplicate\n",
    "print(\"\\nDuplicate rows:\", df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24df641c-fed2-4730-b11c-63202b74ec2a",
   "metadata": {},
   "source": [
    "# Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5337509-b16a-4a40-8c06-bc586b4e2c60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['unnamed:_0', 'method', 'user_agent', 'pragma', 'cache_control', 'accept', 'accept_encoding', 'accept_charset', 'language', 'host', 'cookie', 'content_type', 'connection', 'lenght', 'content', 'classification', 'url']\n"
     ]
    }
   ],
   "source": [
    "# Clean column names\n",
    "df.columns = (\n",
    "    df.columns\n",
    "      .str.strip()\n",
    "      .str.lower()\n",
    "      .str.replace(\"-\", \"_\", regex=False)\n",
    "      .str.replace(\" \", \"_\", regex=False)\n",
    ")\n",
    "\n",
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c9c84d4-12b7-4bb3-95c0-feed3c5345b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"lenght\" in df.columns:\n",
    "    df.rename(columns={\"lenght\": \"length\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa3083bf-2544-42ac-bf7d-5fa948bde543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification\n",
      "0    36000\n",
      "1    25065\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify the target variable\n",
    "print(df[\"classification\"].value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69813e11-5d3e-47cc-9028-647d925d20f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "# Inspect the unique values\n",
    "print(df[\"classification\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c28b7eb-f2ea-4309-b1ca-0c15c2a042b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target\n",
      "0    36000\n",
      "1    25065\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Convert target to binary labels\n",
    "def convert_label(x):\n",
    "    x = str(x).strip().lower()\n",
    "\n",
    "    if x in [\"normal\", \"0\", \"benign\", \"legitimate\"]:\n",
    "        return 0\n",
    "\n",
    "    if x in [\"anomalous\", \"anomaly\", \"attack\", \"malicious\", \"1\"]:\n",
    "        return 1\n",
    "\n",
    "    return np.nan\n",
    "\n",
    "\n",
    "df[\"target\"] = df[\"classification\"].apply(convert_label)\n",
    "\n",
    "print(df[\"target\"].value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "470ffe40-09da-47fd-b290-bc608ccfdbfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final class distribution:\n",
      "target\n",
      "0    36000\n",
      "1    25065\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Remove rows where the target could not be identified\n",
    "df = df.dropna(subset=[\"target\"]).copy()\n",
    "\n",
    "df[\"target\"] = df[\"target\"].astype(int)\n",
    "\n",
    "print(\"\\nFinal class distribution:\")\n",
    "print(df[\"target\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55b7b576-e464-4491-a684-71ca2daee448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unnamed:_0        0\n",
      "method            0\n",
      "url               0\n",
      "classification    0\n",
      "content           0\n",
      "length            0\n",
      "connection        0\n",
      "content_type      0\n",
      "cookie            0\n",
      "host              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Fill missing textual HTTP fields\n",
    "text_columns = [\n",
    "    'content',\n",
    "    'content_type',\n",
    "    'accept'\n",
    "]\n",
    "\n",
    "for col in text_columns:\n",
    "    df[col] = df[col].fillna('')\n",
    "\n",
    "# Fill missing numeric length\n",
    "df['length'] = pd.to_numeric(df['length'], errors='coerce').fillna(0)\n",
    "\n",
    "# Check again\n",
    "print(df.isnull().sum().sort_values(ascending=False).head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d11c8ce-1f80-4f9d-981b-f9ae963b1732",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For HTTP request fields, missing values can generally be represented as empty strings.\n",
    "text_columns = [\n",
    "    \"method\",\n",
    "    \"user_agent\",\n",
    "    \"pragma\",\n",
    "    \"cache_control\",\n",
    "    \"accept\",\n",
    "    \"accept_encoding\",\n",
    "    \"accept_charset\",\n",
    "    \"language\",\n",
    "    \"host\",\n",
    "    \"cookie\",\n",
    "    \"content_type\",\n",
    "    \"content\",\n",
    "    \"url\"\n",
    "]\n",
    "\n",
    "text_columns = [\n",
    "    col for col in text_columns\n",
    "    if col in df.columns\n",
    "]\n",
    "\n",
    "for col in text_columns:\n",
    "    df[col] = df[col].fillna(\"\").astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d736e7d-4933-4663-b272-3651f1eda3e5",
   "metadata": {},
   "source": [
    "# Exploratory data analysis  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8821af3a-d1b0-40e2-91ae-8a950e43d2c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Normal and Malicious HTTP Requests\n",
    "plt.figure(figsize=(7, 5))\n",
    "\n",
    "sns.countplot(\n",
    "    data=df,\n",
    "    x=\"target\"\n",
    ")\n",
    "\n",
    "plt.title(\"Distribution of Normal and Malicious HTTP Requests\")\n",
    "plt.xlabel(\"Class (0 = Normal, 1 = Malicious)\")\n",
    "plt.ylabel(\"Number of Requests\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3690aec8-02e2-4369-aa38-44f4b6878d0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method\n",
      "GET     43088\n",
      "POST    17580\n",
      "PUT       397\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine HTTP request methods\n",
    "if \"method\" in df.columns:\n",
    "\n",
    "    print(df[\"method\"].value_counts())\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "\n",
    "    sns.countplot(\n",
    "        data=df,\n",
    "        x=\"method\",\n",
    "        order=df[\"method\"].value_counts().index\n",
    "    )\n",
    "\n",
    "    plt.title(\"HTTP Request Methods\")\n",
    "    plt.xlabel(\"HTTP Method\")\n",
    "    plt.ylabel(\"Number of Requests\")\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "550126da-48b9-48ac-bb94-a7081bcc9802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    61065.000000\n",
      "mean        90.325358\n",
      "std         75.547066\n",
      "min         31.000000\n",
      "25%         57.000000\n",
      "50%         59.000000\n",
      "75%         77.000000\n",
      "max        895.000000\n",
      "Name: url_length, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# URL length analysis\n",
    "if \"url\" in df.columns:\n",
    "\n",
    "    df[\"url_length\"] = df[\"url\"].str.len()\n",
    "\n",
    "    print(df[\"url_length\"].describe())\n",
    "# Visualisation\n",
    "plt.figure(figsize=(9, 5))\n",
    "\n",
    "sns.histplot(\n",
    "    data=df,\n",
    "    x=\"url_length\",\n",
    "    hue=\"target\",\n",
    "    bins=50,\n",
    "    kde=True\n",
    ")\n",
    "\n",
    "plt.title(\"URL Length Distribution by Class\")\n",
    "plt.xlabel(\"URL Length\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b53d6afa-15e1-429f-a93b-ae44e7dec0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    61065.000000\n",
      "mean        31.936903\n",
      "std         75.660779\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%         17.000000\n",
      "max        836.000000\n",
      "Name: content_length, dtype: float64\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Request content length\n",
    "if \"content\" in df.columns:\n",
    "\n",
    "    df[\"content_length\"] = df[\"content\"].str.len()\n",
    "\n",
    "    print(df[\"content_length\"].describe())\n",
    "plt.figure(figsize=(9, 5))\n",
    "\n",
    "sns.histplot(\n",
    "    data=df,\n",
    "    x=\"content_length\",\n",
    "    hue=\"target\",\n",
    "    bins=50,\n",
    "    kde=True\n",
    ")\n",
    "\n",
    "plt.title(\"HTTP Content Length Distribution\")\n",
    "plt.xlabel(\"Content Length\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2afa123-f0fd-40d4-9d16-c5f4d142adb3",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "836ad68e-e6f5-4284-8137-0b4b4a533cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of URL parameters\n",
    "if \"url\" in df.columns:\n",
    "\n",
    "    df[\"url_parameter_count\"] = (\n",
    "        df[\"url\"]\n",
    "        .str.count(\"&\") +\n",
    "        (df[\"url\"].str.contains(r\"\\?\", regex=True)).astype(int)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2aa229b0-4b29-446d-b796-4f1b9b989182",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Special-character features created successfully.\n",
      "['url_special_<', 'url_special_>', \"url_special_'\", 'url_special_\"', 'url_special_;', 'url_special_=', 'url_special_&', 'url_special_%', 'url_special_/', 'url_special_\\\\\\\\', 'url_special_(', 'url_special_)', 'url_special_{', 'url_special_}', 'url_special_[', 'url_special_]', 'url_special_|']\n"
     ]
    }
   ],
   "source": [
    "# Special character counts in URL\n",
    "if \"url\" in df.columns:\n",
    "\n",
    "    special_chars = [\n",
    "        \"<\", \">\", \"'\", '\"',\n",
    "        \";\", \"=\", \"&\",\n",
    "        \"%\", \"/\", \"\\\\\",\n",
    "        \"(\", \")\", \"{\", \"}\",\n",
    "        \"[\", \"]\", \"|\"\n",
    "    ]\n",
    "\n",
    "    # Make sure URL values are strings\n",
    "    df[\"url\"] = df[\"url\"].fillna(\"\").astype(str)\n",
    "\n",
    "    import re\n",
    "\n",
    "    for char in special_chars:\n",
    "\n",
    "        # Escape character so it is treated literally by regex\n",
    "        safe_name = char.encode(\"unicode_escape\").decode()\n",
    "\n",
    "        df[f\"url_special_{safe_name}\"] = (\n",
    "            df[\"url\"].str.count(re.escape(char))\n",
    "        )\n",
    "\n",
    "print(\"Special-character features created successfully.\")\n",
    "\n",
    "# Display the newly created columns\n",
    "special_columns = [\n",
    "    col for col in df.columns\n",
    "    if col.startswith(\"url_special_\")\n",
    "]\n",
    "\n",
    "print(special_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c5af11f-baa5-497d-b51a-d0e260b0228d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suspicious keyword indicators\n",
    "if \"url\" in df.columns:\n",
    "\n",
    "    full_http_text = (\n",
    "        df[\"url\"].fillna(\"\") + \" \" +\n",
    "        df[\"content\"].fillna(\"\")\n",
    "    ).str.lower()\n",
    "\n",
    "    suspicious_patterns = {\n",
    "        \"has_select\": r\"\\bselect\\b\",\n",
    "        \"has_union\": r\"\\bunion\\b\",\n",
    "        \"has_insert\": r\"\\binsert\\b\",\n",
    "        \"has_update\": r\"\\bupdate\\b\",\n",
    "        \"has_delete\": r\"\\bdelete\\b\",\n",
    "        \"has_drop\": r\"\\bdrop\\b\",\n",
    "        \"has_script\": r\"<script\",\n",
    "        \"has_alert\": r\"\\balert\\s*\\(\",\n",
    "        \"has_onerror\": r\"\\bonerror\\b\",\n",
    "        \"has_exec\": r\"\\bexec\\b\",\n",
    "        \"has_cmd\": r\"\\bcmd\\b\",\n",
    "        \"has_shell\": r\"\\bshell\\b\",\n",
    "        \"has_comment\": r\"--|/\\*|\\*/\",\n",
    "        \"has_encoded\": r\"%[0-9a-fA-F]{2}\"\n",
    "    }\n",
    "\n",
    "    for feature, pattern in suspicious_patterns.items():\n",
    "\n",
    "        df[feature] = (\n",
    "            full_http_text\n",
    "            .str.contains(pattern, regex=True)\n",
    "            .astype(int)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76edfbea-8754-4bd5-9c4f-d1f12f7d0747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Request method encoding\n",
    "if \"method\" in df.columns:\n",
    "\n",
    "    df[\"method_encoded\"] = (\n",
    "        df[\"method\"]\n",
    "        .str.upper()\n",
    "        .map({\n",
    "            \"GET\": 0,\n",
    "            \"POST\": 1,\n",
    "            \"PUT\": 2,\n",
    "            \"DELETE\": 3,\n",
    "            \"HEAD\": 4,\n",
    "            \"OPTIONS\": 5\n",
    "        })\n",
    "        .fillna(-1)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bed13f3a-3055-4756-95a1-f5fb212ea672",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of digits and special characters\n",
    "request_text = (\n",
    "    df[\"url\"].fillna(\"\") + \" \" +\n",
    "    df[\"content\"].fillna(\"\")\n",
    ")\n",
    "\n",
    "df[\"digit_count\"] = request_text.str.count(r\"\\d\")\n",
    "\n",
    "df[\"special_character_count\"] = (\n",
    "    request_text\n",
    "    .str.count(r\"[^a-zA-Z0-9\\s]\")\n",
    ")\n",
    "\n",
    "df[\"uppercase_count\"] = (\n",
    "    request_text\n",
    "    .str.count(r\"[A-Z]\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dcc4532c-15b4-4213-8881-4b017cd4a541",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Request entropy\n",
    "from collections import Counter\n",
    "\n",
    "def entropy(text):\n",
    "\n",
    "    text = str(text)\n",
    "\n",
    "    if not text:\n",
    "        return 0.0\n",
    "\n",
    "    counts = Counter(text)\n",
    "    probabilities = np.array(list(counts.values())) / len(text)\n",
    "\n",
    "    return float(\n",
    "        -np.sum(\n",
    "            probabilities * np.log2(probabilities)\n",
    "        )\n",
    "    )\n",
    "\n",
    "\n",
    "df[\"request_entropy\"] = request_text.apply(entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17dba296-aee2-4ef3-ad86-bc4fff9bcaac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engineered features:\n",
      "['url_length', 'content_length', 'url_parameter_count', 'url_special_<', 'url_special_>', \"url_special_'\", 'url_special_\"', 'url_special_;', 'url_special_=', 'url_special_&', 'url_special_%', 'url_special_/', 'url_special_\\\\\\\\', 'url_special_(', 'url_special_)', 'url_special_{', 'url_special_}', 'url_special_[', 'url_special_]', 'url_special_|', 'has_select', 'has_union', 'has_insert', 'has_update', 'has_delete', 'has_drop', 'has_script', 'has_alert', 'has_onerror', 'has_exec', 'has_cmd', 'has_shell', 'has_comment', 'has_encoded', 'method_encoded', 'digit_count', 'special_character_count', 'uppercase_count', 'request_entropy']\n"
     ]
    }
   ],
   "source": [
    "# Inspect engineered features\n",
    "engineered_features = [\n",
    "    col for col in df.columns\n",
    "    if col.startswith(\"url_\")\n",
    "    or col.startswith(\"has_\")\n",
    "    or col in [\n",
    "        \"url_length\",\n",
    "        \"content_length\",\n",
    "        \"digit_count\",\n",
    "        \"special_character_count\",\n",
    "        \"uppercase_count\",\n",
    "        \"request_entropy\",\n",
    "        \"method_encoded\"\n",
    "    ]\n",
    "]\n",
    "\n",
    "print(\"Engineered features:\")\n",
    "print(engineered_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6bdcdbab-22ed-4e8a-b25e-953543b36a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation analysis\n",
    "numeric_features = [\n",
    "    col for col in engineered_features\n",
    "    if pd.api.types.is_numeric_dtype(df[col])\n",
    "]\n",
    "\n",
    "# Include target\n",
    "corr = df[numeric_features + [\"target\"]].corr()\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "\n",
    "sns.heatmap(\n",
    "    corr,\n",
    "    annot=True,     \n",
    "    fmt=\".2f\",       \n",
    "    cmap=\"coolwarm\",\n",
    "    center=0,\n",
    "    linewidths=0.5\n",
    ")\n",
    "\n",
    "plt.title(\"Correlation Between Engineered Features and Target\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15e5eb7-d7eb-40cf-845d-95b1fa3f4963",
   "metadata": {},
   "source": [
    "# Data Preparation For Modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40779c3c-cb51-4f43-a410-4d06a4eb6ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>url</th>\n",
       "      <th>content</th>\n",
       "      <th>target</th>\n",
       "      <th>request_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GET</td>\n",
       "      <td>http://localhost:8080/tienda1/index.jsp HTTP/1.1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>METHOD_GET http://localhost:8080/tienda1/index...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GET</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/anadir.j...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>METHOD_GET http://localhost:8080/tienda1/publi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POST</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/anadir.j...</td>\n",
       "      <td>id=3&amp;nombre=Vino+Rioja&amp;precio=100&amp;cantidad=55&amp;...</td>\n",
       "      <td>0</td>\n",
       "      <td>METHOD_POST http://localhost:8080/tienda1/publ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GET</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/autentic...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>METHOD_GET http://localhost:8080/tienda1/publi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POST</td>\n",
       "      <td>http://localhost:8080/tienda1/publico/autentic...</td>\n",
       "      <td>modo=entrar&amp;login=choong&amp;pwd=d1se3ci%F3n&amp;remem...</td>\n",
       "      <td>0</td>\n",
       "      <td>METHOD_POST http://localhost:8080/tienda1/publ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  method                                                url  \\\n",
       "0    GET   http://localhost:8080/tienda1/index.jsp HTTP/1.1   \n",
       "1    GET  http://localhost:8080/tienda1/publico/anadir.j...   \n",
       "2   POST  http://localhost:8080/tienda1/publico/anadir.j...   \n",
       "3    GET  http://localhost:8080/tienda1/publico/autentic...   \n",
       "4   POST  http://localhost:8080/tienda1/publico/autentic...   \n",
       "\n",
       "                                             content  target  \\\n",
       "0                                                          0   \n",
       "1                                                          0   \n",
       "2  id=3&nombre=Vino+Rioja&precio=100&cantidad=55&...       0   \n",
       "3                                                          0   \n",
       "4  modo=entrar&login=choong&pwd=d1se3ci%F3n&remem...       0   \n",
       "\n",
       "                                        request_text  \n",
       "0  METHOD_GET http://localhost:8080/tienda1/index...  \n",
       "1  METHOD_GET http://localhost:8080/tienda1/publi...  \n",
       "2  METHOD_POST http://localhost:8080/tienda1/publ...  \n",
       "3  METHOD_GET http://localhost:8080/tienda1/publi...  \n",
       "4  METHOD_POST http://localhost:8080/tienda1/publ...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the text representation\n",
    "df[\"request_text\"] = (\n",
    "    \"METHOD_\" + df[\"method\"].fillna(\"\").astype(str) + \" \" +\n",
    "    df[\"url\"].fillna(\"\").astype(str) + \" \" +\n",
    "    df[\"content\"].fillna(\"\").astype(str)\n",
    ")\n",
    "# Inspect\n",
    "df[\n",
    "    [\"method\", \"url\", \"content\", \"target\", \"request_text\"]\n",
    "].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "142f4ebc-00aa-4eba-bd27-2b4f2e7739e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 48852\n",
      "Testing samples: 12213\n"
     ]
    }
   ],
   "source": [
    "# Train/test split\n",
    "X = df[\"request_text\"]\n",
    "y = df[\"target\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.20,\n",
    "    random_state=RANDOM_STATE,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(\"Training samples:\", len(X_train))\n",
    "print(\"Testing samples:\", len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7b26cc-1bc2-4b85-9b31-d7380467808e",
   "metadata": {},
   "source": [
    "# TF-IDF vectorisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e8d4431-c613-411f-b3fa-d8256283ca28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF training shape: (48852, 50000)\n",
      "TF-IDF testing shape: (12213, 50000)\n"
     ]
    }
   ],
   "source": [
    "# TF-IDF vectorisation\n",
    "tfidf = TfidfVectorizer(\n",
    "    analyzer=\"char\",\n",
    "    ngram_range=(2, 5),\n",
    "    min_df=2,\n",
    "    max_features=50000,\n",
    "    sublinear_tf=True\n",
    ")\n",
    "\n",
    "X_train_tfidf = tfidf.fit_transform(X_train)\n",
    "\n",
    "X_test_tfidf = tfidf.transform(X_test)\n",
    "\n",
    "print(\"TF-IDF training shape:\", X_train_tfidf.shape)\n",
    "print(\"TF-IDF testing shape:\", X_test_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c774a1e-2c9b-4430-9788-76d9d6a4b0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add numerical engineered features\n",
    "train_idx, test_idx = train_test_split(\n",
    "    np.arange(len(df)),\n",
    "    test_size=0.20,\n",
    "    random_state=RANDOM_STATE,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "X_train = df.loc[train_idx, \"request_text\"]\n",
    "X_test = df.loc[test_idx, \"request_text\"]\n",
    "\n",
    "y_train = df.loc[train_idx, \"target\"]\n",
    "y_test = df.loc[test_idx, \"target\"]\n",
    "\n",
    "X_num_train = df.loc[train_idx, numeric_features]\n",
    "X_num_test = df.loc[test_idx, numeric_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5cd670bd-c77a-4a66-ab99-1d7d8ea2319f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit TF-IDF again\n",
    "tfidf = TfidfVectorizer(\n",
    "    analyzer=\"char\",\n",
    "    ngram_range=(2, 5),\n",
    "    min_df=2,\n",
    "    max_features=50000,\n",
    "    sublinear_tf=True\n",
    ")\n",
    "\n",
    "X_train_tfidf = tfidf.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e20341ce-78ff-49c4-aca5-be8287204630",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale numerical features\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_num_train_scaled = scaler.fit_transform(\n",
    "    X_num_train\n",
    ")\n",
    "\n",
    "X_num_test_scaled = scaler.transform(\n",
    "    X_num_test\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0629006a-9728-4cbd-9a46-75fee6e620d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to sparse\n",
    "X_num_train_sparse = csr_matrix(\n",
    "    X_num_train_scaled\n",
    ")\n",
    "\n",
    "X_num_test_sparse = csr_matrix(\n",
    "    X_num_test_scaled\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a8c4a3da-9b91-4074-9d44-7b8d32d9877b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training matrix: (48852, 50039)\n",
      "Final testing matrix: (12213, 50039)\n"
     ]
    }
   ],
   "source": [
    "# Combine\n",
    "X_train_final = hstack([\n",
    "    X_train_tfidf,\n",
    "    X_num_train_sparse\n",
    "]).tocsr()\n",
    "\n",
    "X_test_final = hstack([\n",
    "    X_test_tfidf,\n",
    "    X_num_test_sparse\n",
    "]).tocsr()\n",
    "\n",
    "print(\"Final training matrix:\", X_train_final.shape)\n",
    "print(\"Final testing matrix:\", X_test_final.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392c6b7a-7473-4f3b-8995-a7868356dcab",
   "metadata": {},
   "source": [
    "# Machine Learning Modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "974642cf-6401-478c-80f1-bb867166ac87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the five ML algorithms\n",
    "models = {\n",
    "\n",
    "    \"Logistic Regression\":\n",
    "        LogisticRegression(\n",
    "            max_iter=2000,\n",
    "            random_state=RANDOM_STATE\n",
    "        ),\n",
    "\n",
    "    \"Naive Bayes\":\n",
    "        MultinomialNB(),\n",
    "\n",
    "    \"Decision Tree\":\n",
    "        DecisionTreeClassifier(\n",
    "            random_state=RANDOM_STATE\n",
    "        ),\n",
    "\n",
    "    \"Random Forest\":\n",
    "        RandomForestClassifier(\n",
    "            n_estimators=200,\n",
    "            random_state=RANDOM_STATE,\n",
    "            n_jobs=-1\n",
    "        ),\n",
    "\n",
    "    \"SVM\":\n",
    "        LinearSVC(\n",
    "            random_state=RANDOM_STATE\n",
    "        )\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9eb69314-a99f-48bd-aec7-7d1dfb21d367",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important adjustment for Naïve Bayes\n",
    "nb_X_train = X_train_tfidf\n",
    "nb_X_test = X_test_tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ac05a92-25ad-4c36-b175-c3b7def61bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the remaining models\n",
    "other_X_train = X_train_final\n",
    "other_X_test = X_test_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "886d5ff9-fdb3-436b-98c0-e28aeaa0162d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training: Logistic Regression\n",
      "Completed.\n",
      "\n",
      "Training: Naive Bayes\n",
      "Completed.\n",
      "\n",
      "Training: Decision Tree\n",
      "Completed.\n",
      "\n",
      "Training: Random Forest\n",
      "Completed.\n",
      "\n",
      "Training: SVM\n",
      "Completed.\n"
     ]
    }
   ],
   "source": [
    "# Train all five models\n",
    "trained_models = {}\n",
    "\n",
    "predictions = {}\n",
    "\n",
    "probabilities = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "\n",
    "    print(\"\\nTraining:\", name)\n",
    "\n",
    "    if name == \"Naive Bayes\":\n",
    "\n",
    "        model.fit(\n",
    "            nb_X_train,\n",
    "            y_train\n",
    "        )\n",
    "\n",
    "        pred = model.predict(\n",
    "            nb_X_test\n",
    "        )\n",
    "\n",
    "        if hasattr(model, \"predict_proba\"):\n",
    "            score = model.predict_proba(\n",
    "                nb_X_test\n",
    "            )[:, 1]\n",
    "\n",
    "    else:\n",
    "\n",
    "        model.fit(\n",
    "            other_X_train,\n",
    "            y_train\n",
    "        )\n",
    "\n",
    "        pred = model.predict(\n",
    "            other_X_test\n",
    "        )\n",
    "\n",
    "        if hasattr(model, \"predict_proba\"):\n",
    "            score = model.predict_proba(\n",
    "                other_X_test\n",
    "            )[:, 1]\n",
    "\n",
    "        else:\n",
    "            score = model.decision_function(\n",
    "                other_X_test\n",
    "            )\n",
    "\n",
    "    trained_models[name] = model\n",
    "    predictions[name] = pred\n",
    "    probabilities[name] = score\n",
    "\n",
    "    print(\"Completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48cd00a3-873d-43cc-87d2-c44696d9d8a2",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e4844f0b-03e2-4ff2-a86d-beb0bb761868",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation function\n",
    "def evaluate_model(y_true, y_pred, y_score):\n",
    "\n",
    "    accuracy = accuracy_score(\n",
    "        y_true,\n",
    "        y_pred\n",
    "    )\n",
    "\n",
    "    precision = precision_score(\n",
    "        y_true,\n",
    "        y_pred,\n",
    "        zero_division=0\n",
    "    )\n",
    "\n",
    "    recall = recall_score(\n",
    "        y_true,\n",
    "        y_pred,\n",
    "        zero_division=0\n",
    "    )\n",
    "\n",
    "    f1 = f1_score(\n",
    "        y_true,\n",
    "        y_pred,\n",
    "        zero_division=0\n",
    "    )\n",
    "\n",
    "    auc_score = roc_auc_score(\n",
    "        y_true,\n",
    "        y_score\n",
    "    )\n",
    "\n",
    "    cm = confusion_matrix(\n",
    "        y_true,\n",
    "        y_pred\n",
    "    )\n",
    "\n",
    "    tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "    false_positive_rate = fp / (fp + tn)\n",
    "\n",
    "    return {\n",
    "        \"Accuracy\": accuracy,\n",
    "        \"Precision\": precision,\n",
    "        \"Recall\": recall,\n",
    "        \"F1-Score\": f1,\n",
    "        \"ROC-AUC\": auc_score,\n",
    "        \"False Positive Rate\": false_positive_rate,\n",
    "        \"False Negatives\": fn,\n",
    "        \"False Positives\": fp\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9417b13e-f1ba-4e0c-9baa-9e16c2867a83",
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "      <th>ROC-AUC</th>\n",
       "      <th>False Positive Rate</th>\n",
       "      <th>False Negatives</th>\n",
       "      <th>False Positives</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.995824</td>\n",
       "      <td>0.996995</td>\n",
       "      <td>0.992819</td>\n",
       "      <td>0.994903</td>\n",
       "      <td>0.999509</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.992795</td>\n",
       "      <td>0.994577</td>\n",
       "      <td>0.987832</td>\n",
       "      <td>0.991193</td>\n",
       "      <td>0.992041</td>\n",
       "      <td>0.003750</td>\n",
       "      <td>61</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.988209</td>\n",
       "      <td>0.992116</td>\n",
       "      <td>0.979054</td>\n",
       "      <td>0.985542</td>\n",
       "      <td>0.999327</td>\n",
       "      <td>0.005417</td>\n",
       "      <td>105</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.983870</td>\n",
       "      <td>0.984312</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>0.980270</td>\n",
       "      <td>0.998731</td>\n",
       "      <td>0.010833</td>\n",
       "      <td>119</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.822239</td>\n",
       "      <td>0.715173</td>\n",
       "      <td>0.942150</td>\n",
       "      <td>0.813119</td>\n",
       "      <td>0.931043</td>\n",
       "      <td>0.261250</td>\n",
       "      <td>290</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Precision    Recall  F1-Score   ROC-AUC  \\\n",
       "4                  SVM  0.995824   0.996995  0.992819  0.994903  0.999509   \n",
       "2        Decision Tree  0.992795   0.994577  0.987832  0.991193  0.992041   \n",
       "3        Random Forest  0.988209   0.992116  0.979054  0.985542  0.999327   \n",
       "0  Logistic Regression  0.983870   0.984312  0.976262  0.980270  0.998731   \n",
       "1          Naive Bayes  0.822239   0.715173  0.942150  0.813119  0.931043   \n",
       "\n",
       "   False Positive Rate  False Negatives  False Positives  \n",
       "4             0.002083               36               15  \n",
       "2             0.003750               61               27  \n",
       "3             0.005417              105               39  \n",
       "0             0.010833              119               78  \n",
       "1             0.261250              290             1881  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare all models\n",
    "results = []\n",
    "\n",
    "for name in models.keys():\n",
    "\n",
    "    result = evaluate_model(\n",
    "        y_test,\n",
    "        predictions[name],\n",
    "        probabilities[name]\n",
    "    )\n",
    "\n",
    "    result[\"Model\"] = name\n",
    "\n",
    "    results.append(result)\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "results_df = results_df[\n",
    "    [\n",
    "        \"Model\",\n",
    "        \"Accuracy\",\n",
    "        \"Precision\",\n",
    "        \"Recall\",\n",
    "        \"F1-Score\",\n",
    "        \"ROC-AUC\",\n",
    "        \"False Positive Rate\",\n",
    "        \"False Negatives\",\n",
    "        \"False Positives\"\n",
    "    ]\n",
    "]\n",
    "\n",
    "results_df.sort_values(\n",
    "    \"F1-Score\",\n",
    "    ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f9fee4de-a36e-4843-a303-50a7578a36e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "Logistic Regression\n",
      "======================================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       0.98      0.99      0.99      7200\n",
      "   Malicious       0.98      0.98      0.98      5013\n",
      "\n",
      "    accuracy                           0.98     12213\n",
      "   macro avg       0.98      0.98      0.98     12213\n",
      "weighted avg       0.98      0.98      0.98     12213\n",
      "\n",
      "\n",
      "======================================================================\n",
      "Naive Bayes\n",
      "======================================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       0.95      0.74      0.83      7200\n",
      "   Malicious       0.72      0.94      0.81      5013\n",
      "\n",
      "    accuracy                           0.82     12213\n",
      "   macro avg       0.83      0.84      0.82     12213\n",
      "weighted avg       0.85      0.82      0.82     12213\n",
      "\n",
      "\n",
      "======================================================================\n",
      "Decision Tree\n",
      "======================================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       0.99      1.00      0.99      7200\n",
      "   Malicious       0.99      0.99      0.99      5013\n",
      "\n",
      "    accuracy                           0.99     12213\n",
      "   macro avg       0.99      0.99      0.99     12213\n",
      "weighted avg       0.99      0.99      0.99     12213\n",
      "\n",
      "\n",
      "======================================================================\n",
      "Random Forest\n",
      "======================================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       0.99      0.99      0.99      7200\n",
      "   Malicious       0.99      0.98      0.99      5013\n",
      "\n",
      "    accuracy                           0.99     12213\n",
      "   macro avg       0.99      0.99      0.99     12213\n",
      "weighted avg       0.99      0.99      0.99     12213\n",
      "\n",
      "\n",
      "======================================================================\n",
      "SVM\n",
      "======================================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       1.00      1.00      1.00      7200\n",
      "   Malicious       1.00      0.99      0.99      5013\n",
      "\n",
      "    accuracy                           1.00     12213\n",
      "   macro avg       1.00      1.00      1.00     12213\n",
      "weighted avg       1.00      1.00      1.00     12213\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification reports\n",
    "for name in models.keys():\n",
    "\n",
    "    print(\"\\n\" + \"=\" * 70)\n",
    "    print(name)\n",
    "    print(\"=\" * 70)\n",
    "\n",
    "    print(\n",
    "        classification_report(\n",
    "            y_test,\n",
    "            predictions[name],\n",
    "            target_names=[\n",
    "                \"Normal\",\n",
    "                \"Malicious\"\n",
    "            ],\n",
    "            zero_division=0\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a4be2160-177b-402a-bc84-ec7cd6f173bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix for every model\n",
    "for name in models.keys():\n",
    "\n",
    "    cm = confusion_matrix(\n",
    "        y_test,\n",
    "        predictions[name]\n",
    "    )\n",
    "\n",
    "    plt.figure(figsize=(6, 5))\n",
    "\n",
    "    sns.heatmap(\n",
    "        cm,\n",
    "        annot=True,\n",
    "        fmt=\"d\",\n",
    "        cmap=\"Blues\",\n",
    "        xticklabels=[\"Normal\", \"Malicious\"],\n",
    "        yticklabels=[\"Normal\", \"Malicious\"]\n",
    "    )\n",
    "\n",
    "    plt.title(\n",
    "        f\"Confusion Matrix - {name}\"\n",
    "    )\n",
    "\n",
    "    plt.xlabel(\"Predicted Class\")\n",
    "    plt.ylabel(\"Actual Class\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5497f7b0-db27-4c28-b617-7aaf6f1e985f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curves\n",
    "plt.figure(figsize=(9, 7))\n",
    "\n",
    "for name in models.keys():\n",
    "\n",
    "    y_score = probabilities[name]\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(\n",
    "        y_test,\n",
    "        y_score\n",
    "    )\n",
    "\n",
    "    roc_auc = auc(\n",
    "        fpr,\n",
    "        tpr\n",
    "    )\n",
    "\n",
    "    plt.plot(\n",
    "        fpr,\n",
    "        tpr,\n",
    "        label=f\"{name} (AUC = {roc_auc:.3f})\"\n",
    "    )\n",
    "\n",
    "plt.plot(\n",
    "    [0, 1],\n",
    "    [0, 1],\n",
    "    linestyle=\"--\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "\n",
    "plt.title(\n",
    "    \"ROC Curves for Web Application Attack Detection\"\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "24e556bd-20a8-4231-8b01-8f8db94b76f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmTMqVKiQwXuC91mXLl104MABLViwwPLFwNNu3rypKlWq6Ny5cxoyZIhWrlyp//77T6tXr367xQJ4Kwim8MF7ch2pO3fuyMnJSba2tmrYsKFu3LihpUuX6ubNm7p9+7YiIyPl6uqqJEmSaMSIEZo3b546dOigL7/80jLeiRMnlD59ekn/mxLPrCm8DQ8ePFC3bt105swZZciQQeXLl1exYsUUERGh//77T+XKlZOPj4+CgoK0aNEi7d+/X6tWrXrumE8GXsD7aP78+Xrw4IEaNmwoSbp69aq8vLzUr18/tW/fXs7Ozlq+fLn69Omj2rVrq3v37pZt7969q1mzZunEiRM6d+6cAgMDVbVqVaN2BYhTz/r9PmHCBI0cOVIuLi7at2+fpX3gwIGaN2+eevfurdq1a0uSzp49q27duikyMlLjx4+Xh4fHW6sfH66yZcvKzc1N8+bNkyStWrVKR48eVWhoqLy8vFS2bFlly5ZNx48f1+zZszV06FClTJlSCRMm1Pr165U8eXKD9wBAXCOYwkeje/fu+vPPP9WuXTtVr15dI0aM0KBBg9SgQQMdP35cDx480Pbt2/XJJ59o7ty58vLyUpUqVWRjY6Px48crXbp0VuPxgR5x6UUh55IlS9S2bVtlyJBBZcuW1aZNm3Tx4kVNmzbNcnrSX3/9pdmzZ2vs2LEKCAjQgwcP9PPPP8vd3T3GeMySwvsutsX/Hz16pAQJEqhfv34aN26cVq9erdy5c0uSmjRpotDQUPXr108FCxbkPYAPRmz/fjz5/ti8ebMcHR3l6+urpEmT6u7du/rmm2+0YsUKrVq1StmzZ5cknTlzRiNHjtTEiRNVrFgxeXt7a/HixSpUqJAmT54sLy+vt79z+CCtWbNGZcqUUZEiRXT69Gk5ODgoTZo0unjxou7duycbGxv9888/lv4jR47UoEGDVLhwYc2ePdsymw/Ah4NgCh+8LVu2qG7dukqePLm6dOkib29vFSpUSJcvX9aYMWO0d+9elSpVSkmTJlXhwoWVJUsWffvtt+revbtmzpypI0eOqE+fPnJ2djZ6V/CBevID8oEDBxQVFSVvb295enrKbDbrzp07atOmjfLmzav27dtLkmbNmqUWLVqoePHiWrlypdV43377rcaPHy9XV1f9888/sre3f+v7BBjhyQ/jKVOmVIUKFTR06FAlSZJEe/fuVZs2bfTFF1+oX79+SpgwocHVAm/uyS/Jbt68KVdXV8t74O+//1a9evV05coVmUwmJU+eXIMGDVLp0qW1YcMG9e7dW/ny5dOoUaOsxpw9e7ZOnTqls2fPqmbNmgoICHjr+4UP3/Lly7VlyxYlSZJEhQoVkqenpzJkyKA//vhDderU0bBhwyxnLISHh+vcuXMx1sQE8OEgmMIHI7Z1pCSpadOmsre318SJE19qnCJFiqhu3bpq2bJlfJYLWL1OT548qZYtW+rIkSNKlCiR7t27p127dlmmq69fv1558uTRnTt31K5dO23YsEHlypVTSEiIxo0bp3r16llmi0RGRmrZsmVq1aqVli5dqvz58xu5m8Abe/L3+4tEn769cOFC1alTRytWrFCpUqVkMpnUpUsXLVu2TFOnTmWNHLzXnvxC4/r166pVq5ZcXFw0c+ZMubq66v79+6pQoYK8vb01ZswY7du3T3PmzNGvv/6qbdu2KUuWLOrRo4c2bdqkfv36qVSpUlZLHwBG2bhxo2rUqKFZs2ZxtWvgI8IcdnwQnry8fXh4uOXD/qNHj3T69GndvXtXkjRv3jxNnjxZnTp10rp16yQ9/hbm0KFD2rp1q4oVK6br16+rRIkSkv43PT76cuRAXIp+nXbo0EGZMmWSl5eXNm3apIkTJypJkiTq2rWrpW/x4sXl4OCg1q1bS5IOHjyocePGKXPmzBo2bJgkWUIpW1tbZc6cWd7e3rp169bb3zEgDkVfjdLGxkb3799/Yf/oD9Y1atRQ/vz5NWrUKJ0/f16S1K1bN02ePJlQCu+9J69GmSJFCtnb22vEiBFydXWVJO3du1f79u3T119/rSRJkqh48eKaNm2aUqRIoSFDhkiSAgMD5e7urpkzZ+rRo0dWoRTfW8MI9+7d04oVK/Tpp58qb968RpcD4C3iaxF8EKKnsQ8cOFC7d++Wl5eXSpcurSpVqqhq1ar67rvv5Orqqvz58ysiIkIRERGaMmWK9u7dq7CwME2ZMkW///67ypcvr1WrVsnBwUHS/4ID1iFBfAkKCtLYsWM1YcIEtWjRQpKUPn165c+f33Lp5GibNm3Sli1btHXrVqVOnVpXr15VRESE/vrrL33zzTcaNWqU5b3g7u6uc+fOyc3N7a3vExAXomeERM+C7dSpk3bv3q0SJUqoUqVKyp079zPXiYoOaCdOnKjs2bNr7dq1atCggZImTcrl7fFBOHr0qCpXrqwHDx5o/fr1KliwoNXjiRMn1v379y1rDD548ECOjo7q16+fvvzyS40ZM0a5cuVSwYIFFRwcrE2bNqlUqVKW7bmoC96WU6dOadu2bbpz546GDx8uBwcHTZ8+nQXOgY8MwRTeS9HfoEf/4XTmzBlVq1ZN9+/fV5s2bbR79251795dFy9eVNu2bfXpp5/K1tZW7u7u8vDwUJIkSeTq6qrdu3erSpUqioqK0sCBA5UmTRpJLGyO+Bd9Gl9AQID+/PNP7d271/LY5s2btXDhQpUvX15BQUFq2bKlvLy85OLiInt7e504cUJZsmTRokWLlCtXLnXr1s3qSpFhYWEKDAxUunTpuOQ93lvRgVNoaKjGjh2rHTt2qHDhwpo7d642btyoxYsXK0mSJLGGU7a2toqMjNQnn3yiSZMmqWLFinzBgA/KqVOnZGdnpxo1aliFUtELRru5uSlPnjyaMGGCvv/+e8sXHV5eXkqePLn+/vtv+fv7q1mzZipbtqzlIhrA2/bXX39p7NixsrW11ddff622bdsaXRIAA7DGFN47T67Lc/nyZSVLlkxTp05VSEiIlixZIknas2ePihcvrgwZMmj79u1KkCCB1bYzZ87UhAkTNHv2bGXMmNEydvQpe3yAwds0ePBgrVy5UvXr19emTZsUEhKiGjVqKHny5Bo/fryyZcumyZMny9PTUx06dNDy5cuVOnVqXbhwQcHBwapYsaKk/72+Hz58qK1bt6p48eIG7xnw+h48eKD+/ftr4cKFypMnj3788Ud5eHho7dq16t27tz777DP9+OOPsV6dT4r9qn3A+y76df3gwQONHDlSs2fP1urVq5UmTRo1bdpUc+fO1ZgxY9SkSRP17t1bq1ev1vDhw1WsWDFJ0g8//KCff/5ZmzZtijErFzDKkSNHlDFjRtY4Az5ivPvx3oj+Y8xkMik8PFwtW7bUxYsXNX78eG3fvl05cuRQVFSUmjVrpl9++UUNGzZUjx49LKHU1q1bdejQIf3666/avn27Bg8ebBVKSQRSeLuiZ3rUqVNHO3bsULt27RQQEKCDBw9aZjpVr15d/v7+2r17txo1aqQJEyaoUqVKunbtmpo3b275I+7JD+EODg6EUnivxLbosqOjo7y8vBQeHi5J8vDwkCQVKlRItWrV0qhRo9S4cWPlyZMn1lmuhFL4EJlMJpnNZjk6Oqps2bLasmWLypYtq8uXLytHjhzasGGDPv/8c0lSvXr1dPbsWZUrV0716tWTyWTS3Llz1adPHzk4OBDe4p3h5+dndAkADEYwhffOpk2bdPr0aV28eFFDhw5VihQpdP36df37779KkSKFcuTIofXr1+uzzz6TJO3YscMSWu3YsUOZMmXSggULWHsHb8Wz1sCR/heE+vr6qnr16jp79qxKliwpHx8fywf19OnTy9bWVjdu3JAkubq6KjAw0DJGdD8+XOB98+SH4uhQavHixUqbNq28vb3l5eWlqlWravv27dq5c6fu3bunhAkTWj6Qr1u3Tv3799fSpUs59Roflej3Te7cuVW5cmUNGDBAtWrV0o8//mjVL2vWrJo9e7ayZcumEydO6NKlS1q7di1XagUAvHMIpvBOe/JDvclk0l9//aVixYrJ29tbw4cPV/bs2SVJ1apVU4MGDfTtt98qKCjIsv2pU6c0adIk1alTR6VKlVKuXLmUKFEiSY/XkbKxseEDPeLNk7M4wsPDZW9vH6NP9IfzqlWrauPGjVq+fLlKly5t+fbwp59+UpYsWWKdAWU2m5n2jvfKsmXL5O7uri+++MLqd+/SpUvVvHlzubq66tGjR0qYMKHmzp2r3Llzq169ejpy5IhGjBihPn36SHr8gfvLL7/UV199pTlz5ujLL780apeAeBHbLMInRf/bUbp0aW3dulUHDx7Uo0ePlCBBAsvfTtH/7dat21usHACAV8d5S3gnRURESHo8oyQsLEzLly/X7du3lS1bNrVu3Vrnz59XkiRJLP3r1aunXLlyafv27fr555919uxZrVmzRrVq1dJ///1nWRg6OpSKioqSra0toRTila2trW7duqV27dqpbdu2+u6773T06FFJ/1vPLPq0jESJEqlmzZoKDw/X/PnzFRoaqoCAAPXt21ft2rVTjhw5YozP6xfvk/v372vAgAExLkN/7tw59erVSx07dtTBgwe1fPly+fj4qEmTJjp69KiKFy+ugIAAzZs3T8ePH7dsV6hQIX399dfKlCnT294VIN5Fh1I7d+60zJh9UvTvfx8fH1WuXFm3bt3SyJEjrfqwPAEA4H3Bv1h4J9nZ2SkqKkp//fWXvvrqK/Xs2dNy1bJBgwbJwcFBmzdv1v379y3bBAcHK3Xq1GrcuLECAwNVu3ZtlShRQqtXr1a6dOmsxuePNcSn6A/eq1atUrp06XT06FF5eXlp9erVatasmY4ePSobGxtLv+gPGGXLllWBAgU0evRo+fj4KEmSJDp79qyaNm1q2L4AcSEyMlJOTk7asWOHChUqpMjISMtjGzZs0M2bN1WnTh05OTkpZ86cmjt3ri5duqR58+bJ0dFRlSpVkpeXlwYOHGjZLk2aNBowYIDltG3gfRb9ZUX0vwuLFy+Wh4eH6tatq6JFi2rHjh0xtonuW7x4cZUqVUrz5s3TqVOnrP59AQDgfcCnc7yT5s6dK1dXVw0YMEChoaE6fvy4li5dqsuXL8vNzU09e/bUxIkTLbNPJClHjhyaPHmy/vrrL33//ff6999/NXToUEmy+hAExIcnPwSYTCZFRkbq/9q787Cqyv3//88NyCAiTmmZihmVWkraEXPqaOmBnHEIUnFK05xDyyHnMTWHNKdMHDIcIwQ1QzMHIEkhUxMNzQFTUVFRQUHY6/sHP1aidU6/T+gWez2uyyv2vfZarPtqs/der3Xf73vlypUMHDiQrVu3MnbsWNq0aUNMTAwhISHm83LlXpT4+/vTs2dPDh48yJo1ayhSpIg5glCkIModoQo5NwXWr19P586dOXz4MJBTrD81NRUPDw9ztbHixYvTo0cPVqxYAUDt2rV55ZVX2LFjB0ePHrVZX0TuhzvLFuR+FoSFhTFjxgy++uorChUqxPjx481w6u6bGsWLF+f11183R9zeuU1ERKQgsBi6pSIPmStXrpirLo0ePZrffvuNOXPmsGzZMhYvXkzLli0BKFeuHK+//jozZ87Ezc3tD1eXUR0peZB+++03IiMjefPNN7ly5QrNmjVj+fLluLq60rNnTw4cOMDw4cPp06fPX1qmOzs7G4vFohF+UiBZrVYMwzBDqWvXrlG0aFE2bdpEYGAgU6ZMoUePHiQkJODn50evXr0YMmSIWVsnNDSUvn37EhsbS4UKFUhKSsLe3p6yZcvauGci+ePOOoTJyckMGTKE9PR0XnzxRU6cOMHChQtxdHTkp59+okePHjRs2JAJEybg7Ox8z3eerKwsjh49yvPPP2+r7oiIiPyf6WpHbObPRoHs27ePlJQUfH19AXjyySeZOnUqjz32GCtXruTEiRMAzJ07lyVLlvDdd98Bf3x3UHWk5H6wWq3mXe07TZkyhTlz5uDs7ExaWhrnzp1j1KhRvPjii3h4eBAfH09QUBBOTk5/OC3j7t9hb2+vUEoKjGvXrpk/G4aBnZ0d9vb2HDlyhDfffJMxY8aQmZlJs2bNaN68OZ9//jkJCQl4enrSunVr5s6dy6lTp8zaOpGRkdSvX58KFSoAUL58eYVS8kjJDaWOHz9Ot27dSElJwcHBgTFjxpCUlISjoyMAXl5eZpHzLVu2APd+53FwcFAoJSIiBZaueMRmci8+1q9fz+7duzl9+jQAZcqU4cKFC7i7uwOQnp4OwKhRowgNDWXHjh1kZ2fj5+fHyy+/zKVLl2zTAflHWbFiBfPnzwdypiPdGRhlZmYC4Ofnx8mTJ0lOTsbT05MGDRoQHh7Ol19+SXBwMOXLlwdg69athISEmK/5P6JASgqSadOmERgYSHJyMvB7Uf8RI0ZQo0YNnJ2dqV27NufOnQNgwoQJJCUlsXbtWuzt7enTpw/PPPMM3t7edOjQgRYtWrB69WoCAgJs2S2RfHX3JIWLFy8SEBBAmzZt8PT0ZPPmzaxZs4b333+fCxcu8PXXX5vP7devH05OTmzcuJGzZ88+6FMXERG5r3TlI/fN/5olum7dOh5//HFzOkfLli2JjY2levXq1K5dmw8++AAAFxcXAIoVK4adnR1hYWEcOnQIgJ07d9K9e/f72xERICYmhmrVqpmPDx48yMiRIwHMu9ouLi54enqyb98+AEaOHIm9vT3R0dHExsaSkpLCkiVL6Nu3L46OjpQsWfLBd0QkH+W+z/v6+jJ58mTKlCljbvv+++/ZvHkzkZGRLF26lICAADw8PADw8PAgMDCQdevWsWvXLp566im++eYbhg0bhru7O+XLlycxMZG2bdvapF8i+ckwDLKysu4Z5VS4cGEqVapEUlISFStWNNs7depEuXLl+Pzzz7l16xYAjz/+OO3btycyMpKYmJgHefoiIiL3nWpMyX1xZ92E3Hoh8HuBz9OnT+Pv70/btm0ZMmQIN2/epFWrVhw9epQffviBnTt30qFDB0JCQmjatClFihRh6tSpJCUlsWrVKj7++GM6deqE1Wo1v+hpyp7cD3cWpQW4ceMGRYoUYenSpQwePJgWLVowZswYKlWqxKVLl6hWrRqLFy+mefPmACxatIjg4GB+/fVXPDw8OHXqFNOmTaNbt2626pJIvsmtc5P73/3792NnZ0f16tUJDQ2lW7dunDx5ktTUVH766ScuXrxIZmYmffr0ISsri3/961/UqVOH4cOHm1P27v6bEynI7qwFdePGDTZt2kS1atUoV64cRYsWZf/+/bzzzjsULVqUb775xtxvzpw5hISE0KtXL/Pzwmq1EhkZaZY6EBEReWQYIvkoOzvb/PnKlStGUFCQ0a1bN2PkyJFGenq6uW3q1KlGrVq1DMMwjJSUFCMwMNBwd3c3goKCjNu3bxsZGRnGBx98YBQrVsyoXbu24e3tbZQuXdq4evWq8eqrrxrt27d/4H0T+frrr426desaW7ZsMQzDMOLi4owXXnjB8PX1NX766SfDMAzD19fX6NKlS579Lly4YMTGxhqbNm3K037n34tIQXP79u08j1NSUozixYsbAwYMMNLT0434+HijUaNGRsmSJY1nnnnGaN68ufH8888bbm5uxnvvvWcYhmEEBwcbjo6OxldffWWDHojkv/DwcCMhIeGe9pkzZxqFCxc2KleubJQtW9aoX7++ce7cOcMwDGPhwoXGc889Z6xfv958fnJystGhQwejdu3axunTpx/Y+YuIiNiCbklKvsq9yz1y5Egee+wxfvnlF7Kzs/n444/p3LmzOSTd2dmZ559/nkmTJvH000+TkpLC7t27mTFjBg4ODjg6OjJx4kRWrlxJ06ZNadKkCb/++ivu7u6kp6fzxBNP2LKb8ojLXU3sbpUqVSIjI4OIiAguXrxIzZo1mT9/Pm5ubrRs2ZKoqCi8vLywWq2kpqaa+z322GN4e3vTtGlT4PfC/xoVIgVR7t9G7kjY5cuXc/LkSUqUKEFQUBDbtm0jOjqaGjVqsGrVKiZPnszKlSuZNGkShw4don///qxbtw6Abt268dlnn9G6dWtbdUck3+SuMBkWFkZGRobZvnv3bubMmcPKlSuJjo4mLCyMs2fP0r9/f1JSUmjZsiU1a9Zk9uzZ5udD6dKladq0KXXq1KFw4cK26pKIiMiDYetkTB4t58+fNxo0aGAULlzYiIuLMwzDMG7evGl8/PHHhsViMWJjYw3DyLlz6OrqalSqVMmIiIgw98/KyjKmT5+e567hnaZPn2489dRTxq5du+5/Z+QfKSsry/w5MTHROHr0qHHr1i2zbfr06UatWrWMZcuWmW1Wq9Xw9fU1WrVqZVSqVMlo1KjRAz1nEVtYtmyZUbp0aePFF180ZsyYYba/+OKLRpcuXYxTp07ds09GRobRrl07Y+TIkXn+1kQKutzX84ABAwxvb28jKirK3DZkyBDD29vbSE9PN58XFRVlFC9e3Fi9erVhGIaxbt0646WXXjImTZr04E9eRETExnS7XvJVdnY2Hh4eeHh4ULNmTSBndNTFixext7cnKSkJgJ49e/LUU0/h7e2Nt7e3uf+GDRvYsGFDnmXHz5w5w6JFi6hcuTJz585lzpw5NGjQ4MF2TB55VqsVyFm++/Lly7Rr144GDRrQokUL/Pz8iIqKAuCdd96hRIkSbN68mePHjwM59c0+/fRTGjVqxIkTJ4iNjeXy5cs264vI/bZp0ybGjx/PuHHjiImJoXPnzua2ESNGsH37dnbu3AnA9evXiYiIYNq0aVStWpXjx4/TsWNHsw6hSEF3Z13N0aNHc+XKFcLCwsxVKq1WK5mZmbi4uGBvb09WVhb16tWjdu3afPHFFwD8+9//xsvLiy+//JLr16/brC8iIiK2oGBK8lXZsmXp1asXN27cYN68eQD06NGDSZMmYWdnR8WKFbly5QpFihRh+PDhnDhxAi8vL/r160eTJk3o0qULfn5+eQpDP/HEEzz77LOMGDGCU6dOmUWlRfLDwoULgZxpdYZhEBcXR+PGjTEMgy1bthAeHo6DgwNTp07lyJEjuLq60rVrVxITEwkLCzOPU758eQYOHMi2bdu4evUqJUqUsFGPRPJPdnb2H7YvWbIELy8vevfujaOjI6VKlTKn+LVv357q1asTEhLC8ePHyczMJD4+no0bNxIUFER8fDyVK1d+kN0Qua9yQ6mZM2eyevVqypQpw9dff82ePXuAnNDpwoULREZGAr/fCKlRowZnz54lMzOTxx57jKFDh7Jjxw7c3Nxs0xEREREbUTAl+e7FF1+ke/fuvP/++7i7u3PmzBlWr17NyJEjGT9+PPXq1SMuLo4OHTqwceNGevbsiZOTEy+88AKnTp0iKCgI+L2Oib29PY0aNcpzR14kPxw+fJg+ffowduxYIGfkk7OzM40bN2b9+vV4eXlx5swZYmJiiI+PZ/HixQAEBATwwgsv8O233xIdHQ38fqHx6quvUqhQIbNOiEhBdOcIQoC0tDTzPfnatWtcv34dT09P8znGHSuPAUyYMIGDBw+ycuVK3N3dGTBgADt27KBPnz4PuCci95dhGFy/fp1WrVoxa9Ys7OzsqFChAqdOnWLZsmVcvHiRWrVqUb9+fYYPH45hGDg6OgKwZ88emjVrZj5+9tlnFUqJiMg/ksUw/qDCr8h/YRgGhmH818LNP/30E4MGDeLmzZvmHUOAmzdv8uabb3Ls2DFq1KjBtGnT7ilknp2djZ2dXZ6LHJG/4+6L5lwZGRnMmjWLCRMmcPbsWbO4fmZmJgDdu3fnhx9+4N133yUxMZHo6GjmzJlDo0aNiIqKomPHjgwcOJB3331Xr1d5JK1atYpp06bx+OOPY29vz6effkrZsmV57bXXKFasGAsWLKB06dJYrVbs7Ow4c+YM7u7uuLm50blzZ0qVKsWHH35oXniLFHS50/bu/FxJTEykcePGLFq0CF9fXwDWr1/PW2+9xezZs+nWrRt79+6lQ4cOWCwWGjRowM8//8yFCxdYu3Yt//rXv2zZJREREZvTiCn5/yU7OxuLxYKdnR1Xrlwx76DfnW9WrVqVTp06cfjwYWJjYwG4desWLi4uLF++nEmTJrFnzx4uXLiQZz+r1Yq9vb0u8iVfWSwWbt68yeTJkwkJCTHbnZycaN++PZUqVaJXr15ATk20YsWKsXjxYq5du0ZkZCSDBw+mWbNmHD9+nCVLlpCRkUH9+vVZtWoVQUFBer1KgZT7vp07OurOdsMwGDlyJEOGDDED2MzMTN544w0OHz7MiBEjiIyMZMuWLcDvK0wuWrSIBQsWABAcHMzMmTMVSskj4e5RhHe+78fHx5ORkZEnYGrXrh21a9dm/vz5HDlyhFq1avHdd9/RvXt3bt++ba42rFBKREREwZT8BXdOScq9Szho0CDq1avHihUrAO65MC9UqBBNmjShYcOG5tQ8Z2dnDMPA3d2dVq1akZiYiJeXV579/tsoLJH/q6ysLIYMGcLIkSPp1KkTgwcPNouZe3h4MGzYMNauXUtcXBx2dnbcuHGDmJgYSpQoQdWqVQHYv38/Tz/9NMeOHWPv3r0A1K1bF7j3wl7kYZb7erVYLOYI1Vy5o0DS0tLYtWsXc+fOZciQIfj6+lKsWDEOHTpEYmIir732Gn5+fkyfPp2mTZuyaNEifHx8CA4OplKlSgAqbi6PhLtHiX/22We0aNGCYcOG8eOPPwJQu3ZtLly4YD6+efMmAO+//z4//fQTYWFhpKamUq5cOYYNG8bSpUuZMGGCbTokIiLyEFIKIH9o+/bt1KhRAwAHBwez/fDhw1StWpXY2FgmT57Mk08++afHqFChAr169eLYsWNmbZ67R1b9WWFdkfzk4OBAq1ataNSoEd7e3iQkJODn50dwcDCpqan4+/vj4+NDjx49AHB1daVEiRIkJyezbNkyFi9ezNatWxk3bhxhYWHUr18/z/EVqEpBkPv+m/t6XbhwIV27dqVly5Y0a9aMjRs3mquBxcbGcubMGdq0acNHH31E8eLFuXz5Mt9++y2tWrUCYN68eUycOBGr1cqaNWt48sknSUhIoF27dsC9NyxECiKLxYLFYuHixYvma75KlSqsXLmS4cOHExsbS8WKFfHz82PIkCEAuLi4ABAXF0ehQoVYsmQJiYmJ5jEV2oqIiOSlGlPyh2JiYvjtt99o3769WTsEYOLEiURHR7Nq1SqKFSv2P4+TkpLCwIEDuX79Ohs2bLjPZy3y3w0ZMoSff/6ZXr16cf78eebOnUuhQoVYunQpV69exc/Pj1mzZtGtWzeioqJYsGAB0dHRWCwWJk2aRIcOHYA/r1klUhBs2LCB/v37U7RoUfz9/UlKSuLXX38lJiaG3r17M3PmTM6ePYuXlxeGYVCmTBlGjRpFQEAAAElJSZw6dQpvb28cHR3Jzs7m5s2bFClSxMY9E8l/hmGwePFiQkJCKF68OKNGjaJmzZrExMQwadIk3NzcWL16NfHx8bz22mu0bNkSf39/nnjiCSZOnEhgYCCGYeDn52frroiIiDy0FExJnuDp7gvulJQUnJ2dcXV15datWzRt2pRy5cqZU/jg90Kgf+bSpUuUKlXq/nVA5C/68ccfGTZsGGXLlmXp0qUkJycTGBjIxYsXqVSpEoUKFWLPnj2cPHnS3OfIkSN5lrZXKCUF1dWrV+nduzdr165l/vz5dOnSxRzZATnF/jdv3szEiRPp0aMHgYGB7Ny5k8OHD+cJncaNG0dqaioTJ06kcOHCtuiKyH3xZ99ngoODmTp1Kq6ursTHx5vtn3zyCUuWLGHw4MF06tSJyMhIRo0axfnz50lJSSEgIIDFixfrM0NEROR/0PwTMUOpo0eP5vnylJaWRuPGjenYsSOQU6cnKyvLXLkMci7S/9eQ9NxQStP2xNZq1KiBj48PP//8M8uWLaNMmTKEhoYyY8YM4uPjiY6O5vTp00yaNMncJzeUyq21pgsMKaiSkpL45ZdfeOONN+jduzcuLi5YrVbzvXnEiBF4eXkxd+5c0tPT6dKlC/b29vTo0YOwsDBiY2PNKbC1a9dWKCWPDMMwzMVXIKdsQVJSkrm9Y8eONGnShMuXL5t1pABat27NCy+8wPLly7l06RL/+c9/2LZtG+Hh4Rw5coTPPvtMnxkiIiJ/gYIpAcDPz4+hQ4dy7do1goOD8ff3x9XVlbfffpvdu3ezZ88eihQpQp06dYiIiODgwYPA7xfpa9euJTIyEri3jlQu1VSQh0FAQAAVK1Zk/fr1nDt3jiJFivDqq6+ybds2Bg8eTPXq1c3lvu90Z601kYKoWrVqdO7cmVOnTrF8+XKzPfe92dPTE19fX86dO8f27dtp3LgxK1asIDExkdGjRxMYGAjA3r178ff3t0kfRP6uP/qOkrva8KFDh6hbty7t2rWjbt26fPDBByQmJuLk5ESbNm146qmnCA4ONvcrV64crVq14uLFi0ydOhUANzc3vLy8KFeu3APrk4iISEGnYOofLnd1pqCgIH744QeqVavG0KFDqVevHgBNmzalTp065sp6U6dOxcXFhYkTJ7J+/Xp+++03wsPDGT9+PNu3b9c0J3nolS1bltatW3P58mU+//xzs/3pp59m0KBB7N+/n5deesmGZyhy/7zxxhuUL1+edevWcf78eezs7PKMmmrbti1XrlwhNTUVgAYNGhAdHc23335LZGQkX331FaVLl7ZlF0T+T+5cjfJOuUFVSEgIfn5+1KhRg40bNzJ16lT27dvHlClTAHj11VepX78++/btY+PGjeb+Pj4++Pj44O3t/YB6IiIi8uhRMPUPlTstKXca35EjR7h06RJWq5WIiAgGDBgAgIeHB2+//TZHjx5l2bJlAMyfP5+SJUvSoUMHWrRoQWBgIJ07d+bDDz9UKCUFQtu2balevTqff/45+/fvv2e7pp3Koyo3mE1JSWHp0qVAzudA7qipGzdu4OzsTKFChcx9nJ2deeyxx6hYsaItTlnkb7l7NcqVK1cya9Ystm/fTmZmpvm9xWq10rFjR+bNm0elSpW4ePEiUVFRbNmyhdWrVwM5wW7p0qVZuXIlaWlpQM4IqSlTptC+fXsb9E5EROTRoOLn/yC5/6vvDI9WrVpFlSpVKF++PD/++CNvvfUWvXv3pl+/fri5uQE5BdBHjhzJli1bOHHiBJBz4X748GHOnz9PvXr1zFojdxZSF3mYbd26lejoaN59913c3d1tfToiD8ytW7cYNGgQR44c4eOPP8bLy4vs7Gxu377NqFGj2LNnD1u3bsXZ2dnWpyqSb8LCwhgwYABFixbFycmJkydP0q1bNz766CMg54bdzZs3OX36NF27diU9PZ3evXsTGRnJ1atX2bZtG05OTnz00Ud8+umnzJw5k+bNm9u4VyIiIo8GJQiPuE2bNrFixQqys7OxWCxmKBUeHo6HhwfTpk1j06ZNODo60rhxY9544w1Wr17Nvn37zGOULFmSzp074+joyODBg4GcmiTVqlWjSZMmFC5c+J4RWCIPuyZNmjB27FiFUvKP4+zsbNaI+uyzz4Cc9/QpU6bwzTff0K9fP5ydnf+0XqBIQXL16lUCAgJo06YNI0aMYN++fWzYsAF/f39mzpzJsWPHgJw6gq6urkyfPh0vLy+2bdtG//79qVChAnv37mX27NkAdO3alVWrVimUEhERyUdKER5x69ato1ChQtjb25sXGREREQQFBdGvXz++//77PKOjxo4dS0ZGBmFhYVy4cME8Tq1atWjdujU7duwgIyPjnt+jwtBSUOXWHRH5J2nUqBGvvPIKBw4cYOjQoTz33HOEhIQwZ84cM7TS1Gx5FNy9GqWzszPlypWjePHiODs751l9Lzk5ma1bt+Ll5cUTTzxBZmYmaWlpVKlShTVr1nDlyhVKlSqlOoQiIiL5TMHUIyq3Rs7SpUt58803sVqtZqD0ww8/8Pjjj/Pee+/h4ODArVu3uHr1KsnJybi6uhIUFMTmzZtZtWoVp0+fpmnTphw9epTx48cTFxeHk5OTLbsmkq80yk/+qQICAkhLS2PBggW89dZbJCYm0rBhQ1uflki+unM1yrCwMAA++eQT5syZQ+HChfOsnnfz5k1eeuklIiIi+OGHHxg9ejQpKSnMnj2bXbt2Ubx4cRv1QkRE5NGmGlOPoDvrPBmGQVxcHIMGDWL48OE0a9aMjz/+mFmzZuHv78+vv/5KZmYmMTExlC1blg0bNlCxYkW6dOnCgQMHOHnyJC+88AIREREUK1YMyKnDoBFSIiIF3549e6hZsyaOjo62PhWR++bs2bMMGjSIM2fOkJKSgtVq5bXXXiM7O5vjx49TpUoVZs6ciZOTE6GhoUyfPp2kpCTc3d1ZtmwZtWrVsnUXREREHmkKph4hdxc3v3DhAqVLlyYjI4PKlSvj6+vLtGnTSE9PZ9GiRWzatAkfHx/Kli3LSy+9RNu2bWndujVz5swhNTWVkydP4ujoSJUqVWzZLREREZG/Zc2aNYwePZoyZcqwa9cusz0sLIzRo0dTvHhxunbtSrdu3UhPT+fYsWNUr17dhmcsIiLyz6Fg6hGRnZ1tLvedkZHB3Llz2b17N1OmTKFq1aosW7aMMWPGMG3aNLN+yN2aN2/Oyy+/zMiRI/O0W61WDMMwjy8iIiJSkGRkZDBw4ECOHj3K7Nmz8fLyMrclJyczZswYli5dSkxMjGpIiYiIPGAqrvKIyA2NBg4cyODBg1m7di3x8fFs27YNyFlF5umnnyYkJIRffvkFyBlhdeLECQ4dOkTHjh1JSEigadOm9xzbzs5OoZSIiIgUWE5OTvj7+2MYBkuWLDHbs7KyKFOmDLNmzeL8+fMKpURERGxAwdQjIjU1lddff50dO3bg6+tLQEAAjo6OhISEEBMTA8C4ceOIi4vjm2++ITs7m+joaKZOnYqvry9Xr15l586d1KxZ08Y9EREREcl/uatR/vjjj4SHhwO/L4Dh4uKi4uYiIiI2omCqgDEMw1ze/s5ZmJcuXeLQoUNMnDiR5s2bExQUxMqVK7lx4wbh4eHcunWLBg0a8J///Ic1a9Zw4MABXn75Zfz8/IiIiGDTpk2UK1fOXM1PRERE5FETEBDA7du3CQ8PJzs7WyuzioiIPAT0aVyAZGdnY7FYsLOzIzMz0yxyDnD06FGysrJ47rnngJzQqk6dOrzyyiuEhoayfft2AMaPH09cXBwbN27EYrHg4+NDjRo1MAwjT50qERERkUdN1apVmTFjBgsWLNB3HhERkYeEgqkCJPcL1JgxY2jevDlvv/02X375JQCNGzcmNTWVHTt2AJgjn3r37s2JEycIDQ3l3LlzlCtXjoiICIYPH57nC5nFYtEXNBEREXnk1atXj0KFCtn6NEREROT/o2CqADl79ize3t6Ehobi4+PDL7/8Qq9evfjoo49wdHSkb9++jB07losXL+Lg4ADAnj178PT0JCEhwVweuXHjxjg4OJhTAkVEREREREREbEHB1EPiznpRf/QYICoqitu3b7Nx40YGDx7M119/zfDhwxkzZgzHjx9nzJgxuLi40K5dO2bMmMFXX33FF198waRJk7h27RoHDx7Mc2zVVRARERERERERW1Iy8ZCwWCxkZGTw3XffmY/vdujQIdLS0vDw8AByVpDp168fzzzzDBMmTKBIkSJERETg6enJkiVL6Nu3L//+979p06YNjz/+OPHx8X96bBERERERERGRB83B1icgObKysnj33XeJjo5mzZo1VK5cGavVmmdUU4kSJXBzc+PAgQNUr14dq9WKk5MT/v7+rF+/nuvXr1O1alWWLFnCpUuXcHNzw8nJicTERM6ePcu4ceNs2EMRERERERERkbw0Yuoh4eDgQLNmzShZsiTBwcHA71PtcqfeeXl54eLiQmhoaJ7thw4dokKFCri5uZl1o5ydnYmPj2fcuHE0bNiQp59+moYNGz7gXomIiIiIiIiI/DkFUw+RZs2aUatWLaKjo/n2228BsFqt5tS7Ro0aUbduXdavX8/YsWM5duwY3377Lfv37zdDp9ywKjs7m4MHD7J582amT59OeHg4pUqVskm/RERERERERET+iMX4oyrbYjP79+9n2LBhlC5dmuDg4Dyr59nZ2ZGcnExoaChDhw7Fw8ODU6dO0b9/fyZNmnTPsdLS0nB1dX3QXRARERERERER+UsUTD2EZs6cydq1a+nTpw+dO3fGMAxz1NSFCxdwdXUlKyuLn3/+mcqVK1OiRAmAe2pSiYiIiIiIiIg8zJRiPIQCAgKoUKECa9euJTk5GYvFQmpqKjNnzqRWrVosXrwYd3d36tatS4kSJcjOzsYwDIVSIiIiIiIiIlKgaMTUQyokJIR58+bRokULXn75Zfr378/p06eZPHkyffv2tfXpiYiIiIiIiIj8bQqmHlIZGRkMHDiQpUuXcvv2bfr06cMnn3xibte0PREREREREREp6BxsfQLyx5ycnHjjjTcoVqwYffr0oUKFCgBkZWXh4OCgUEpERERERERECjyNmCogsrOzsbOzM4ugi4iIiIiIiIgUdBoxVQBYrVbs7e1tfRoiIiIiIiIiIvlKI6ZERERERERERMQmVKhIRERERERERERsQsGUiIiIiIiIiIjYhIIpERERERERERGxCQVTIiIiIiIiIiJiEwqmRERERERERETEJhRMiYiIiIiIiIiITSiYEhERkYfajh07sFgsXL169S/vU7FiRWbPnn3fzklERERE8oeCKREREflbunbtisVioXfv3vds69OnDxaLha5duz74ExMRERGRh56CKREREfnbypcvz+rVq7l586bZduvWLVatWkWFChVseGYiIiIi8jBTMCUiIiJ/W82aNalQoQKhoaFmW2hoKOXLl6dGjRpmW0ZGBgMGDKB06dI4OztTv3599u7dm+dYmzdv5tlnn8XFxYVGjRpx8uTJe35fTEwMr7zyCi4uLpQvX54BAwaQlpZ23/onIiIiIveHgikRERHJF926dWPp0qXm4+DgYLp3757nOe+//z5ffvkly5cvJz4+Hk9PT3x8fLh8+TIASUlJtGnThqZNm7J//3569OjBsGHD8hzj4MGD+Pj40KZNGw4cOMCaNWuIioqiX79+97+TIiIiIpKvFEyJiIhIvggMDCQqKoqTJ09y6tQpoqOj6dSpk7k9LS2NBQsWMH36dF5//XWqVq3K4sWLcXFxYcmSJQAsWLCASpUqMWvWLJ577jk6dux4T32q6dOn06FDBwYNGsQzzzxD3bp1mTNnDitWrODWrVsPsssiIiIi8jc52PoERERE5NFQqlQpmjVrxvLlyzEMg2bNmlGqVClz+/Hjx7l9+zb16tUz2woVKoS3tzcJCQkAJCQk8PLLL2OxWMzn1KlTJ8/viYuL49ixY3zxxRdmm2EYWK1WTpw4QZUqVe5XF0VEREQknymYEhERkXzTvXt3c0rdvHnz8mwzDAMgT+iU257blvuc/8ZqtdKrVy8GDBhwzzYVWhcREREpWDSVT0RERPKNr68vmZmZZGZm4uPjk2ebp6cnjo6OREVFmW23b99m37595iinqlWrsmfPnjz73f24Zs2a/Pzzz3h6et7zz9HR8T71TERERETuBwVTIiIikm/s7e1JSEggISEBe3v7PNtcXV155513eO+999iyZQuHDx+mZ8+epKen89ZbbwHQu3dvjh8/TlBQEEePHiUkJIRly5blOc7QoUP5/vvv6du3L/v37ycxMZHw8HD69+//oLopIiIiIvlEwZSIiIjkq6JFi1K0aNE/3Pbhhx/Stm1bAgMDqVmzJseOHeObb76hePHiQM5UvC+//JKIiAi8vLxYuHAhkydPznOM6tWrs3PnThITE2nQoAE1atRg1KhRPPHEE/e9byIiIiKSvyzGXynmICIiIiIiIiIiks80YkpERERERERERGxCwZSIiIiIiIiIiNiEgikREREREREREbEJBVMiIiIiIiIiImITCqZERERERERERMQmFEyJiIiIiIiIiIhNKJgSERERERERERGbUDAlIiIiIiIiIiI2oWBKRERERERERERsQsGUiIiIiIiIiIjYhIIpERERERERERGxCQVTIiIiIiIiIiJiE/8Ph3XSEj764GsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model performance comparison\n",
    "metrics_to_plot = [\n",
    "    \"Accuracy\",\n",
    "    \"Precision\",\n",
    "    \"Recall\",\n",
    "    \"F1-Score\",\n",
    "    \"ROC-AUC\"\n",
    "]\n",
    "\n",
    "plot_df = results_df.set_index(\"Model\")[\n",
    "    metrics_to_plot\n",
    "]\n",
    "\n",
    "plot_df.plot(\n",
    "    kind=\"bar\",\n",
    "    figsize=(12, 6)\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    \"Performance Comparison of Machine Learning Models\"\n",
    ")\n",
    "\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0, 1.05)\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "plt.legend(\n",
    "    bbox_to_anchor=(1.02, 1),\n",
    "    loc=\"upper left\"\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5dcc213b-7b0f-4822-80b1-20ddc557728d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# False-positive comparison\n",
    "plt.figure(figsize=(9, 5))\n",
    "\n",
    "sns.barplot(\n",
    "    data=results_df,\n",
    "    x=\"Model\",\n",
    "    y=\"False Positive Rate\"\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    \"False Positive Rate by Machine Learning Model\"\n",
    ")\n",
    "\n",
    "plt.ylabel(\"False Positive Rate\")\n",
    "plt.xlabel(\"Model\")\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bff8de7c-e146-42a4-993f-d580c0963327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# False-negative comparison\n",
    "plt.figure(figsize=(9, 5))\n",
    "\n",
    "sns.barplot(\n",
    "    data=results_df,\n",
    "    x=\"Model\",\n",
    "    y=\"False Negatives\"\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    \"False Negatives by Machine Learning Model\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"Number of False Negatives\")\n",
    "\n",
    "plt.xticks(rotation=30)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9dd654-31eb-450b-bb76-c7544a7e2ce9",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc500be6-84a8-4ca0-b4ca-e57a0f2d2cae",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0afd5f8d-6155-4499-aa39-59a43988cf64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'C': 10, 'solver': 'liblinear'}\n",
      "Best CV F1:\n",
      "0.9897991509059504\n"
     ]
    }
   ],
   "source": [
    "lr_params = {\n",
    "    \"C\": [0.1, 1, 10],\n",
    "    \"solver\": [\"liblinear\"]\n",
    "}\n",
    "\n",
    "lr_grid = GridSearchCV(\n",
    "    LogisticRegression(\n",
    "        max_iter=2000,\n",
    "        random_state=RANDOM_STATE\n",
    "    ),\n",
    "    lr_params,\n",
    "    scoring=\"f1\",\n",
    "    cv=3,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "lr_grid.fit(\n",
    "    other_X_train,\n",
    "    y_train\n",
    ")\n",
    "\n",
    "print(\"Best parameters:\")\n",
    "print(lr_grid.best_params_)\n",
    "\n",
    "print(\"Best CV F1:\")\n",
    "print(lr_grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239b59e7-db8c-4ae8-adb6-e3c1295e7312",
   "metadata": {},
   "source": [
    "## Decision Tree tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1a0c60c2-90bf-40b6-bc9b-4810a35adea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'criterion': 'gini', 'max_depth': None, 'min_samples_split': 2}\n",
      "Best CV F1:\n",
      "0.9825161013619711\n"
     ]
    }
   ],
   "source": [
    "dt_params = {\n",
    "    \"max_depth\": [5, 10, 20, None],\n",
    "    \"min_samples_split\": [2, 5, 10],\n",
    "    \"criterion\": [\"gini\", \"entropy\"]\n",
    "}\n",
    "\n",
    "dt_grid = GridSearchCV(\n",
    "    DecisionTreeClassifier(\n",
    "        random_state=RANDOM_STATE\n",
    "    ),\n",
    "    dt_params,\n",
    "    scoring=\"f1\",\n",
    "    cv=3,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "dt_grid.fit(\n",
    "    other_X_train,\n",
    "    y_train\n",
    ")\n",
    "\n",
    "print(\"Best parameters:\")\n",
    "print(dt_grid.best_params_)\n",
    "\n",
    "print(\"Best CV F1:\")\n",
    "print(dt_grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61cf212-eb8d-42b0-a885-bb4529002c5c",
   "metadata": {},
   "source": [
    "## Random Forest tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "79ffee58-dfa4-4591-b804-e21b61ea5c54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'max_depth': None, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "Best CV F1:\n",
      "0.9719153698834052\n"
     ]
    }
   ],
   "source": [
    "rf_params = {\n",
    "    \"n_estimators\": [100, 200],\n",
    "    \"max_depth\": [10, 20, None],\n",
    "    \"min_samples_split\": [2, 5]\n",
    "}\n",
    "\n",
    "rf_grid = GridSearchCV(\n",
    "    RandomForestClassifier(\n",
    "        random_state=RANDOM_STATE,\n",
    "        n_jobs=-1\n",
    "    ),\n",
    "    rf_params,\n",
    "    scoring=\"f1\",\n",
    "    cv=3,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "rf_grid.fit(\n",
    "    other_X_train,\n",
    "    y_train\n",
    ")\n",
    "\n",
    "print(\"Best parameters:\")\n",
    "print(rf_grid.best_params_)\n",
    "\n",
    "print(\"Best CV F1:\")\n",
    "print(rf_grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2a041d-a2e8-4ea7-b577-7532e178be18",
   "metadata": {},
   "source": [
    "## SVM tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a86700d9-f66f-401c-875e-b1c9a0c9f01d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'C': 10}\n",
      "Best CV F1:\n",
      "0.99207836379111\n"
     ]
    }
   ],
   "source": [
    "svm_params = {\n",
    "    \"C\": [0.01, 0.1, 1, 10]\n",
    "}\n",
    "\n",
    "svm_grid = GridSearchCV(\n",
    "    LinearSVC(\n",
    "        random_state=RANDOM_STATE\n",
    "    ),\n",
    "    svm_params,\n",
    "    scoring=\"f1\",\n",
    "    cv=3,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "svm_grid.fit(\n",
    "    other_X_train,\n",
    "    y_train\n",
    ")\n",
    "\n",
    "print(\"Best parameters:\")\n",
    "print(svm_grid.best_params_)\n",
    "\n",
    "print(\"Best CV F1:\")\n",
    "print(svm_grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198ecd1a-5b98-4dd7-ae26-3bba19a124cd",
   "metadata": {},
   "source": [
    "## Naïve Bayes tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "497ec111-fbe3-4c5a-839c-53d6038ef81f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'alpha': 0.01}\n",
      "Best CV F1:\n",
      "0.8348858525528685\n"
     ]
    }
   ],
   "source": [
    "nb_params = {\n",
    "    \"alpha\": [0.01, 0.1, 0.5, 1.0]\n",
    "}\n",
    "\n",
    "nb_grid = GridSearchCV(\n",
    "    MultinomialNB(),\n",
    "    nb_params,\n",
    "    scoring=\"f1\",\n",
    "    cv=3,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "nb_grid.fit(\n",
    "    nb_X_train,\n",
    "    y_train\n",
    ")\n",
    "\n",
    "print(\"Best parameters:\")\n",
    "print(nb_grid.best_params_)\n",
    "\n",
    "print(\"Best CV F1:\")\n",
    "print(nb_grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f65868-615b-424b-b07e-f57a774e5a65",
   "metadata": {},
   "source": [
    "# Evaluate tuned models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7cd1ad81-b494-4b2d-8657-610f12f227b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_models = {\n",
    "    \"Logistic Regression\": lr_grid.best_estimator_,\n",
    "    \"Naive Bayes\": nb_grid.best_estimator_,\n",
    "    \"Decision Tree\": dt_grid.best_estimator_,\n",
    "    \"Random Forest\": rf_grid.best_estimator_,\n",
    "    \"SVM\": svm_grid.best_estimator_\n",
    "}\n",
    "tuned_predictions = {}\n",
    "tuned_scores = {}\n",
    "\n",
    "for name, model in tuned_models.items():\n",
    "\n",
    "    if name == \"Naive Bayes\":\n",
    "\n",
    "        Xtr = nb_X_train\n",
    "        Xte = nb_X_test\n",
    "\n",
    "    else:\n",
    "\n",
    "        Xtr = other_X_train\n",
    "        Xte = other_X_test\n",
    "\n",
    "    pred = model.predict(Xte)\n",
    "\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "        score = model.predict_proba(Xte)[:, 1]\n",
    "    else:\n",
    "        score = model.decision_function(Xte)\n",
    "\n",
    "    tuned_predictions[name] = pred\n",
    "    tuned_scores[name] = score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "df3dfba2-da65-4aac-a4b9-a96b5e68e810",
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "      <th>ROC-AUC</th>\n",
       "      <th>False Positive Rate</th>\n",
       "      <th>False Negatives</th>\n",
       "      <th>False Positives</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.995906</td>\n",
       "      <td>0.995606</td>\n",
       "      <td>0.994415</td>\n",
       "      <td>0.995010</td>\n",
       "      <td>0.999194</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.995169</td>\n",
       "      <td>0.996393</td>\n",
       "      <td>0.991821</td>\n",
       "      <td>0.994102</td>\n",
       "      <td>0.999745</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.992795</td>\n",
       "      <td>0.994577</td>\n",
       "      <td>0.987832</td>\n",
       "      <td>0.991193</td>\n",
       "      <td>0.992041</td>\n",
       "      <td>0.003750</td>\n",
       "      <td>61</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.986735</td>\n",
       "      <td>0.991290</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>0.983719</td>\n",
       "      <td>0.999246</td>\n",
       "      <td>0.005972</td>\n",
       "      <td>119</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.840252</td>\n",
       "      <td>0.722335</td>\n",
       "      <td>0.992220</td>\n",
       "      <td>0.836037</td>\n",
       "      <td>0.971382</td>\n",
       "      <td>0.265556</td>\n",
       "      <td>39</td>\n",
       "      <td>1912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Precision    Recall  F1-Score   ROC-AUC  \\\n",
       "4                  SVM  0.995906   0.995606  0.994415  0.995010  0.999194   \n",
       "0  Logistic Regression  0.995169   0.996393  0.991821  0.994102  0.999745   \n",
       "2        Decision Tree  0.992795   0.994577  0.987832  0.991193  0.992041   \n",
       "3        Random Forest  0.986735   0.991290  0.976262  0.983719  0.999246   \n",
       "1          Naive Bayes  0.840252   0.722335  0.992220  0.836037  0.971382   \n",
       "\n",
       "   False Positive Rate  False Negatives  False Positives  \n",
       "4             0.003056               28               22  \n",
       "0             0.002500               41               18  \n",
       "2             0.003750               61               27  \n",
       "3             0.005972              119               43  \n",
       "1             0.265556               39             1912  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final tuned results\n",
    "tuned_results = []\n",
    "\n",
    "for name in tuned_models.keys():\n",
    "\n",
    "    result = evaluate_model(\n",
    "        y_test,\n",
    "        tuned_predictions[name],\n",
    "        tuned_scores[name]\n",
    "    )\n",
    "\n",
    "    result[\"Model\"] = name\n",
    "\n",
    "    tuned_results.append(result)\n",
    "\n",
    "tuned_results_df = pd.DataFrame(\n",
    "    tuned_results\n",
    ")\n",
    "\n",
    "tuned_results_df = tuned_results_df[\n",
    "    [\n",
    "        \"Model\",\n",
    "        \"Accuracy\",\n",
    "        \"Precision\",\n",
    "        \"Recall\",\n",
    "        \"F1-Score\",\n",
    "        \"ROC-AUC\",\n",
    "        \"False Positive Rate\",\n",
    "        \"False Negatives\",\n",
    "        \"False Positives\"\n",
    "    ]\n",
    "]\n",
    "\n",
    "tuned_results_df.sort_values(\n",
    "    \"F1-Score\",\n",
    "    ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "094f0c79-8b1e-4de8-ac84-9abfb59dfebb",
   "metadata": {},
   "source": [
    "# Identify the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bff19aed-57e0-4949-bcf3-dfd4d1850d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model based on F1-score:\n",
      "Model                       SVM\n",
      "Accuracy               0.995906\n",
      "Precision              0.995606\n",
      "Recall                 0.994415\n",
      "F1-Score                0.99501\n",
      "ROC-AUC                0.999194\n",
      "False Positive Rate    0.003056\n",
      "False Negatives              28\n",
      "False Positives              22\n",
      "Name: 4, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Identify the best model\n",
    "best_model_row = tuned_results_df.loc[\n",
    "    tuned_results_df[\"F1-Score\"].idxmax()\n",
    "]\n",
    "\n",
    "print(\"Best model based on F1-score:\")\n",
    "print(best_model_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "12643e16-6a2e-4e89-bfca-d182c0120458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Normal       1.00      1.00      1.00      7200\n",
      "   Malicious       1.00      0.99      1.00      5013\n",
      "\n",
      "    accuracy                           1.00     12213\n",
      "   macro avg       1.00      1.00      1.00     12213\n",
      "weighted avg       1.00      1.00      1.00     12213\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Best model classification report\n",
    "best_model_name = best_model_row[\"Model\"]\n",
    "\n",
    "print(\n",
    "    classification_report(\n",
    "        y_test,\n",
    "        tuned_predictions[best_model_name],\n",
    "        target_names=[\n",
    "            \"Normal\",\n",
    "            \"Malicious\"\n",
    "        ],\n",
    "        zero_division=0\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "06f963dc-d12f-4647-a35c-284147e66893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best model confusion matrix\n",
    "cm = confusion_matrix(\n",
    "    y_test,\n",
    "    tuned_predictions[best_model_name]\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(7, 6))\n",
    "\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    annot=True,\n",
    "    fmt=\"d\",\n",
    "    cmap=\"Blues\",\n",
    "    xticklabels=[\"Normal\", \"Malicious\"],\n",
    "    yticklabels=[\"Normal\", \"Malicious\"]\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    f\"Confusion Matrix - {best_model_name}\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2b649d43-1272-496e-9490-9cd2d748d583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Best model ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(\n",
    "    y_test,\n",
    "    tuned_scores[best_model_name]\n",
    ")\n",
    "\n",
    "roc_auc = auc(\n",
    "    fpr,\n",
    "    tpr\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.plot(\n",
    "    fpr,\n",
    "    tpr,\n",
    "    label=f\"AUC = {roc_auc:.3f}\"\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    [0, 1],\n",
    "    [0, 1],\n",
    "    linestyle=\"--\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "\n",
    "plt.title(\n",
    "    f\"ROC Curve - {best_model_name}\"\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77d66cb-886a-4901-8980-4a677317b3e4",
   "metadata": {},
   "source": [
    "# Cross-validation for the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3a888446-d7e0-4ee4-b144-9e765604423d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: Mean F1 = 0.9927, Std = 0.0017\n",
      "Naive Bayes: Mean F1 = 0.8342, Std = 0.0050\n",
      "Decision Tree: Mean F1 = 0.9865, Std = 0.0014\n",
      "Random Forest: Mean F1 = 0.9787, Std = 0.0010\n",
      "SVM: Mean F1 = 0.9947, Std = 0.0009\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = StratifiedKFold(\n",
    "    n_splits=5,\n",
    "    shuffle=True,\n",
    "    random_state=RANDOM_STATE\n",
    ")\n",
    "\n",
    "for name, model in tuned_models.items():\n",
    "\n",
    "    if name == \"Naive Bayes\":\n",
    "        X_cv = nb_X_train\n",
    "    else:\n",
    "        X_cv = other_X_train\n",
    "\n",
    "    scores = cross_val_score(\n",
    "        model,\n",
    "        X_cv,\n",
    "        y_train,\n",
    "        cv=cv,\n",
    "        scoring=\"f1\",\n",
    "        n_jobs=-1\n",
    "    )\n",
    "\n",
    "    print(\n",
    "        f\"{name}: \"\n",
    "        f\"Mean F1 = {scores.mean():.4f}, \"\n",
    "        f\"Std = {scores.std():.4f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a228fb23-2752-4dbc-b57a-f221c0972895",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f310b8b2-937d-4127-bb41-501447022ce6",
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47812</th>\n",
       "      <td>tt</td>\n",
       "      <td>0.011782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31803</th>\n",
       "      <td>hod_</td>\n",
       "      <td>0.011358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20100</th>\n",
       "      <td>a=</td>\n",
       "      <td>0.008220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26225</th>\n",
       "      <td>da1/</td>\n",
       "      <td>0.007095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>/tie</td>\n",
       "      <td>0.006967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46435</th>\n",
       "      <td>st:</td>\n",
       "      <td>0.006713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26220</th>\n",
       "      <td>da1</td>\n",
       "      <td>0.006632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16504</th>\n",
       "      <td>:8</td>\n",
       "      <td>0.006597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3711</th>\n",
       "      <td>/tien</td>\n",
       "      <td>0.006499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35982</th>\n",
       "      <td>lho</td>\n",
       "      <td>0.006424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21288</th>\n",
       "      <td>alh</td>\n",
       "      <td>0.006395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47848</th>\n",
       "      <td>ttp:/</td>\n",
       "      <td>0.006316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14618</th>\n",
       "      <td>808</td>\n",
       "      <td>0.006156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19624</th>\n",
       "      <td>_ge</td>\n",
       "      <td>0.005628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>//lo</td>\n",
       "      <td>0.005596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5420</th>\n",
       "      <td>1/</td>\n",
       "      <td>0.005547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39850</th>\n",
       "      <td>o=</td>\n",
       "      <td>0.005461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47845</th>\n",
       "      <td>ttp/</td>\n",
       "      <td>0.005243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37136</th>\n",
       "      <td>meth</td>\n",
       "      <td>0.005066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29787</th>\n",
       "      <td>et h</td>\n",
       "      <td>0.004876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Importance\n",
       "47812      tt    0.011782\n",
       "31803    hod_    0.011358\n",
       "20100      a=    0.008220\n",
       "26225    da1/    0.007095\n",
       "3710     /tie    0.006967\n",
       "46435     st:    0.006713\n",
       "26220     da1    0.006632\n",
       "16504      :8    0.006597\n",
       "3711    /tien    0.006499\n",
       "35982     lho    0.006424\n",
       "21288     alh    0.006395\n",
       "47848   ttp:/    0.006316\n",
       "14618     808    0.006156\n",
       "19624     _ge    0.005628\n",
       "3497     //lo    0.005596\n",
       "5420       1/    0.005547\n",
       "39850      o=    0.005461\n",
       "47845    ttp/    0.005243\n",
       "37136    meth    0.005066\n",
       "29787    et h    0.004876"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importance – Random Forest\n",
    "rf_model = tuned_models[\"Random Forest\"]\n",
    "\n",
    "importances = rf_model.feature_importances_\n",
    "\n",
    "tfidf_names = tfidf.get_feature_names_out()\n",
    "\n",
    "all_feature_names = (\n",
    "    list(tfidf_names) +\n",
    "    numeric_features\n",
    ")\n",
    "\n",
    "importance_df = pd.DataFrame({\n",
    "    \"Feature\": all_feature_names,\n",
    "    \"Importance\": importances\n",
    "})\n",
    "\n",
    "importance_df = importance_df.sort_values(\n",
    "    \"Importance\",\n",
    "    ascending=False\n",
    ")\n",
    "\n",
    "importance_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2970de5c-0b48-4371-9bf1-7de7c8eae867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot top Random Forest features\n",
    "top_features = importance_df.head(20)\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.barplot(\n",
    "    data=top_features,\n",
    "    x=\"Importance\",\n",
    "    y=\"Feature\"\n",
    ")\n",
    "\n",
    "plt.title(\n",
    "    \"Top 20 Random Forest Features\"\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bdd36a9b-3224-4857-a7b7-f893c36dd9a5",
   "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>Feature</th>\n",
       "      <th>Coefficient</th>\n",
       "      <th>Absolute_Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20100</th>\n",
       "      <td>a=</td>\n",
       "      <td>30.167381</td>\n",
       "      <td>30.167381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26236</th>\n",
       "      <td>da=</td>\n",
       "      <td>16.292776</td>\n",
       "      <td>16.292776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>%2</td>\n",
       "      <td>14.345796</td>\n",
       "      <td>14.345796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16549</th>\n",
       "      <td>=+</td>\n",
       "      <td>10.847302</td>\n",
       "      <td>10.847302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38253</th>\n",
       "      <td>na=</td>\n",
       "      <td>10.219292</td>\n",
       "      <td>10.219292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>%2f</td>\n",
       "      <td>10.203472</td>\n",
       "      <td>10.203472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>oa=</td>\n",
       "      <td>9.907048</td>\n",
       "      <td>9.907048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8359</th>\n",
       "      <td>2f</td>\n",
       "      <td>9.785692</td>\n",
       "      <td>9.785692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39992</th>\n",
       "      <td>oa</td>\n",
       "      <td>8.687832</td>\n",
       "      <td>8.687832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>%e</td>\n",
       "      <td>-8.569811</td>\n",
       "      <td>8.569811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>+&amp;</td>\n",
       "      <td>7.976722</td>\n",
       "      <td>7.976722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>%27</td>\n",
       "      <td>7.887688</td>\n",
       "      <td>7.887688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31162</th>\n",
       "      <td>gina</td>\n",
       "      <td>7.838356</td>\n",
       "      <td>7.838356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33507</th>\n",
       "      <td>ina=</td>\n",
       "      <td>7.837023</td>\n",
       "      <td>7.837023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31163</th>\n",
       "      <td>gina=</td>\n",
       "      <td>7.837023</td>\n",
       "      <td>7.837023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40251</th>\n",
       "      <td>ogina</td>\n",
       "      <td>7.837023</td>\n",
       "      <td>7.837023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5366</th>\n",
       "      <td>1+</td>\n",
       "      <td>7.809332</td>\n",
       "      <td>7.809332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6686</th>\n",
       "      <td>1a=</td>\n",
       "      <td>7.802658</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000</th>\n",
       "      <td>b1a=</td>\n",
       "      <td>7.802658</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>&amp;b1a</td>\n",
       "      <td>7.802658</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Coefficient  Absolute_Coefficient\n",
       "20100      a=    30.167381             30.167381\n",
       "26236     da=    16.292776             16.292776\n",
       "33         %2    14.345796             14.345796\n",
       "16549      =+    10.847302             10.847302\n",
       "38253     na=    10.219292             10.219292\n",
       "238       %2f    10.203472             10.203472\n",
       "39998     oa=     9.907048              9.907048\n",
       "8359       2f     9.785692              9.785692\n",
       "39992      oa     8.687832              8.687832\n",
       "579        %e    -8.569811              8.569811\n",
       "868        +&     7.976722              7.976722\n",
       "125       %27     7.887688              7.887688\n",
       "31162    gina     7.838356              7.838356\n",
       "33507    ina=     7.837023              7.837023\n",
       "31163   gina=     7.837023              7.837023\n",
       "40251   ogina     7.837023              7.837023\n",
       "5366       1+     7.809332              7.809332\n",
       "6686      1a=     7.802658              7.802658\n",
       "23000    b1a=     7.802658              7.802658\n",
       "765      &b1a     7.802658              7.802658"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression feature analysis\n",
    "lr_model = tuned_models[\"Logistic Regression\"]\n",
    "\n",
    "coefficients = lr_model.coef_[0]\n",
    "\n",
    "lr_features = pd.DataFrame({\n",
    "    \"Feature\": all_feature_names,\n",
    "    \"Coefficient\": coefficients\n",
    "})\n",
    "\n",
    "lr_features[\"Absolute_Coefficient\"] = (\n",
    "    lr_features[\"Coefficient\"].abs()\n",
    ")\n",
    "\n",
    "lr_features = lr_features.sort_values(\n",
    "    \"Absolute_Coefficient\",\n",
    "    ascending=False\n",
    ")\n",
    "\n",
    "lr_features.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "716dac4c-6d92-4348-96c8-b466bdb2645e",
   "metadata": {},
   "source": [
    "# Most malicious-indicating features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "088a5169-a6ec-4995-bc19-4126a867c29c",
   "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>Feature</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20100</th>\n",
       "      <td>a=</td>\n",
       "      <td>30.167381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26236</th>\n",
       "      <td>da=</td>\n",
       "      <td>16.292776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>%2</td>\n",
       "      <td>14.345796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16549</th>\n",
       "      <td>=+</td>\n",
       "      <td>10.847302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38253</th>\n",
       "      <td>na=</td>\n",
       "      <td>10.219292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>%2f</td>\n",
       "      <td>10.203472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>oa=</td>\n",
       "      <td>9.907048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8359</th>\n",
       "      <td>2f</td>\n",
       "      <td>9.785692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39992</th>\n",
       "      <td>oa</td>\n",
       "      <td>8.687832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>+&amp;</td>\n",
       "      <td>7.976722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>%27</td>\n",
       "      <td>7.887688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31162</th>\n",
       "      <td>gina</td>\n",
       "      <td>7.838356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40251</th>\n",
       "      <td>ogina</td>\n",
       "      <td>7.837023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31163</th>\n",
       "      <td>gina=</td>\n",
       "      <td>7.837023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33507</th>\n",
       "      <td>ina=</td>\n",
       "      <td>7.837023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5366</th>\n",
       "      <td>1+</td>\n",
       "      <td>7.809332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6686</th>\n",
       "      <td>1a=</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000</th>\n",
       "      <td>b1a=</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>&amp;b1a</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>&amp;b1a=</td>\n",
       "      <td>7.802658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Coefficient\n",
       "20100      a=    30.167381\n",
       "26236     da=    16.292776\n",
       "33         %2    14.345796\n",
       "16549      =+    10.847302\n",
       "38253     na=    10.219292\n",
       "238       %2f    10.203472\n",
       "39998     oa=     9.907048\n",
       "8359       2f     9.785692\n",
       "39992      oa     8.687832\n",
       "868        +&     7.976722\n",
       "125       %27     7.887688\n",
       "31162    gina     7.838356\n",
       "40251   ogina     7.837023\n",
       "31163   gina=     7.837023\n",
       "33507    ina=     7.837023\n",
       "5366       1+     7.809332\n",
       "6686      1a=     7.802658\n",
       "23000    b1a=     7.802658\n",
       "765      &b1a     7.802658\n",
       "766     &b1a=     7.802658"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_features = (\n",
    "    lr_features\n",
    "    .sort_values(\n",
    "        \"Coefficient\",\n",
    "        ascending=False\n",
    "    )\n",
    "    .head(20)\n",
    ")\n",
    "\n",
    "positive_features[\n",
    "    [\"Feature\", \"Coefficient\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "582b2f5d-a6f2-4cae-a664-a6d56c3ab51d",
   "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>Feature</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>%e</td>\n",
       "      <td>-8.569811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25508</th>\n",
       "      <td>d=</td>\n",
       "      <td>-7.689818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>%2c+</td>\n",
       "      <td>-6.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8236</th>\n",
       "      <td>2c+</td>\n",
       "      <td>-6.436983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23738</th>\n",
       "      <td>c+</td>\n",
       "      <td>-6.222523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>%f</td>\n",
       "      <td>-5.958260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37789</th>\n",
       "      <td>n=</td>\n",
       "      <td>-5.795517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>%24</td>\n",
       "      <td>-5.052232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39850</th>\n",
       "      <td>o=</td>\n",
       "      <td>-4.585816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50001</th>\n",
       "      <td>content_length</td>\n",
       "      <td>-4.469825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40250</th>\n",
       "      <td>ogin=</td>\n",
       "      <td>-4.408766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33321</th>\n",
       "      <td>in=</td>\n",
       "      <td>-4.408766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31134</th>\n",
       "      <td>gin=</td>\n",
       "      <td>-4.408766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7420</th>\n",
       "      <td>24</td>\n",
       "      <td>-4.405042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3062</th>\n",
       "      <td>.jsp</td>\n",
       "      <td>-4.163622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34584</th>\n",
       "      <td>jsp h</td>\n",
       "      <td>-4.163622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34583</th>\n",
       "      <td>jsp</td>\n",
       "      <td>-4.163622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22980</th>\n",
       "      <td>b1=</td>\n",
       "      <td>-4.153621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>1=</td>\n",
       "      <td>-4.153621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>&amp;b1=</td>\n",
       "      <td>-4.153621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Feature  Coefficient\n",
       "579                %e    -8.569811\n",
       "25508              d=    -7.689818\n",
       "210              %2c+    -6.436983\n",
       "8236              2c+    -6.436983\n",
       "23738              c+    -6.222523\n",
       "675                %f    -5.958260\n",
       "37789              n=    -5.795517\n",
       "67                %24    -5.052232\n",
       "39850              o=    -4.585816\n",
       "50001  content_length    -4.469825\n",
       "40250           ogin=    -4.408766\n",
       "33321             in=    -4.408766\n",
       "31134            gin=    -4.408766\n",
       "7420               24    -4.405042\n",
       "3062            .jsp     -4.163622\n",
       "34584           jsp h    -4.163622\n",
       "34583            jsp     -4.163622\n",
       "22980             b1=    -4.153621\n",
       "6630               1=    -4.153621\n",
       "756              &b1=    -4.153621"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most normal-indicating features\n",
    "negative_features = (\n",
    "    lr_features\n",
    "    .sort_values(\n",
    "        \"Coefficient\",\n",
    "        ascending=True\n",
    "    )\n",
    "    .head(20)\n",
    ")\n",
    "\n",
    "negative_features[\n",
    "    [\"Feature\", \"Coefficient\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a8d3e3-5805-4c41-95d7-7b7950cc7693",
   "metadata": {},
   "source": [
    "# Test an individual HTTP request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4fcfc5ee-cdc7-4e81-9f5a-13dd77209b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_http_request(\n",
    "    method,\n",
    "    url,\n",
    "    content=\"\",\n",
    "    model_name=None\n",
    "):\n",
    "\n",
    "    if model_name is None:\n",
    "        model_name = best_model_name\n",
    "\n",
    "    request = (\n",
    "        \"METHOD_\" + str(method) + \" \" +\n",
    "        str(url) + \" \" +\n",
    "        str(content)\n",
    "    )\n",
    "\n",
    "    text_vector = tfidf.transform(\n",
    "        [request]\n",
    "    )\n",
    "\n",
    "    temp = pd.DataFrame({\n",
    "        \"url\": [str(url)],\n",
    "        \"content\": [str(content)],\n",
    "        \"method\": [str(method)]\n",
    "    })\n",
    "\n",
    "    temp[\"url_length\"] = temp[\"url\"].str.len()\n",
    "    temp[\"content_length\"] = temp[\"content\"].str.len()\n",
    "\n",
    "    temp[\"url_parameter_count\"] = (\n",
    "        temp[\"url\"].str.count(\"&\") +\n",
    "        temp[\"url\"].str.contains(\n",
    "            r\"\\?\",\n",
    "            regex=True\n",
    "        ).astype(int)\n",
    "    )\n",
    "\n",
    "    for char in [\n",
    "        \"<\", \">\", \"'\", '\"',\n",
    "        \";\", \"=\", \"&\",\n",
    "        \"%\", \"/\", \"\\\\\",\n",
    "        \"(\", \")\", \"{\", \"}\",\n",
    "        \"[\", \"]\", \"|\"\n",
    "    ]:\n",
    "\n",
    "        safe_name = char.encode(\n",
    "            \"unicode_escape\"\n",
    "        ).decode()\n",
    "\n",
    "        temp[\n",
    "            f\"url_special_{safe_name}\"\n",
    "        ] = temp[\"url\"].str.count(char)\n",
    "\n",
    "    combined = (\n",
    "        temp[\"url\"] + \" \" +\n",
    "        temp[\"content\"]\n",
    "    ).str.lower()\n",
    "\n",
    "    for feature, pattern in suspicious_patterns.items():\n",
    "\n",
    "        temp[feature] = (\n",
    "            combined\n",
    "            .str.contains(\n",
    "                pattern,\n",
    "                regex=True\n",
    "            )\n",
    "            .astype(int)\n",
    "        )\n",
    "\n",
    "    temp[\"method_encoded\"] = (\n",
    "        temp[\"method\"]\n",
    "        .str.upper()\n",
    "        .map({\n",
    "            \"GET\": 0,\n",
    "            \"POST\": 1,\n",
    "            \"PUT\": 2,\n",
    "            \"DELETE\": 3,\n",
    "            \"HEAD\": 4,\n",
    "            \"OPTIONS\": 5\n",
    "        })\n",
    "        .fillna(-1)\n",
    "    )\n",
    "\n",
    "    req = (\n",
    "        temp[\"url\"] + \" \" +\n",
    "        temp[\"content\"]\n",
    "    )\n",
    "\n",
    "    temp[\"digit_count\"] = req.str.count(r\"\\d\")\n",
    "    temp[\"special_character_count\"] = req.str.count(\n",
    "        r\"[^a-zA-Z0-9\\s]\"\n",
    "    )\n",
    "    temp[\"uppercase_count\"] = req.str.count(r\"[A-Z]\")\n",
    "    temp[\"request_entropy\"] = req.apply(entropy)\n",
    "\n",
    "    temp = temp[numeric_features]\n",
    "\n",
    "    temp_scaled = scaler.transform(temp)\n",
    "\n",
    "    temp_final = hstack([\n",
    "        text_vector,\n",
    "        csr_matrix(temp_scaled)\n",
    "    ]).tocsr()\n",
    "\n",
    "    model = tuned_models[model_name]\n",
    "\n",
    "    if model_name == \"Naive Bayes\":\n",
    "\n",
    "        final_vector = text_vector\n",
    "\n",
    "    else:\n",
    "\n",
    "        final_vector = temp_final\n",
    "\n",
    "    prediction = model.predict(\n",
    "        final_vector\n",
    "    )[0]\n",
    "\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "\n",
    "        probability = model.predict_proba(\n",
    "            final_vector\n",
    "        )[0, 1]\n",
    "\n",
    "    else:\n",
    "\n",
    "        decision = model.decision_function(\n",
    "            final_vector\n",
    "        )[0]\n",
    "\n",
    "        probability = 1 / (\n",
    "            1 + np.exp(-decision)\n",
    "        )\n",
    "\n",
    "    label = (\n",
    "        \"Malicious\"\n",
    "        if prediction == 1\n",
    "        else \"Normal\"\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"Prediction\": label,\n",
    "        \"Malicious Score\": round(\n",
    "            float(probability) * 100,\n",
    "            2\n",
    "        ),\n",
    "        \"Model\": model_name\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "19ef15f9-14fd-46e9-b048-82f881ce7087",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example prediction\n",
    "import re\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from collections import Counter\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "\n",
    "# ============================================================\n",
    "# Shannon Entropy\n",
    "# ============================================================\n",
    "\n",
    "def calculate_entropy(text):\n",
    "\n",
    "    if not text:\n",
    "        return 0.0\n",
    "\n",
    "    counts = Counter(text)\n",
    "    length = len(text)\n",
    "\n",
    "    return -sum(\n",
    "        (count / length) * math.log2(count / length)\n",
    "        for count in counts.values()\n",
    "    )\n",
    "\n",
    "\n",
    "# ============================================================\n",
    "# HTTP REQUEST PREDICTION\n",
    "# ============================================================\n",
    "\n",
    "def predict_http_request(\n",
    "    method,\n",
    "    url,\n",
    "    content=\"\",\n",
    "    model_name=None\n",
    "):\n",
    "\n",
    "    # --------------------------------------------------------\n",
    "    # Select model\n",
    "    # --------------------------------------------------------\n",
    "\n",
    "    if model_name is None:\n",
    "        model_name = best_model_name\n",
    "\n",
    "    if model_name not in trained_models:\n",
    "\n",
    "        raise ValueError(\n",
    "            f\"Model '{model_name}' not found.\\n\"\n",
    "            f\"Available models: {list(trained_models.keys())}\"\n",
    "        )\n",
    "\n",
    "    # --------------------------------------------------------\n",
    "    # Create temporary dataframe\n",
    "    # --------------------------------------------------------\n",
    "\n",
    "    temp = pd.DataFrame({\n",
    "        \"method\": [method],\n",
    "        \"url\": [url],\n",
    "        \"content\": [content]\n",
    "    })\n",
    "\n",
    "    # --------------------------------------------------------\n",
    "    # Clean text\n",
    "    # --------------------------------------------------------\n",
    "\n",
    "    temp[\"method\"] = (\n",
    "        temp[\"method\"]\n",
    "        .fillna(\"\")\n",
    "        .astype(str)\n",
    "    )\n",
    "\n",
    "    temp[\"url\"] = (\n",
    "        temp[\"url\"]\n",
    "        .fillna(\"\")\n",
    "        .astype(str)\n",
    "    )\n",
    "\n",
    "    temp[\"content\"] = (\n",
    "        temp[\"content\"]\n",
    "        .fillna(\"\")\n",
    "        .astype(str)\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # BASIC FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    # URL length\n",
    "    temp[\"url_length\"] = (\n",
    "        temp[\"url\"].str.len()\n",
    "    )\n",
    "\n",
    "    # Content length\n",
    "    temp[\"content_length\"] = (\n",
    "        temp[\"content\"].str.len()\n",
    "    )\n",
    "\n",
    "    # URL parameter count\n",
    "    temp[\"url_parameter_count\"] = (\n",
    "        temp[\"url\"].str.count(r\"\\?\")\n",
    "        +\n",
    "        temp[\"url\"].str.count(\"&\")\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # SPECIAL CHARACTER FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    special_chars = [\n",
    "        \"<\",\n",
    "        \">\",\n",
    "        \"'\",\n",
    "        '\"',\n",
    "        \";\",\n",
    "        \"=\",\n",
    "        \"&\",\n",
    "        \"%\",\n",
    "        \"/\",\n",
    "        \"\\\\\",\n",
    "        \"(\",\n",
    "        \")\",\n",
    "        \"{\",\n",
    "        \"}\",\n",
    "        \"[\",\n",
    "        \"]\",\n",
    "        \"|\"\n",
    "    ]\n",
    "\n",
    "    for char in special_chars:\n",
    "\n",
    "        safe_name = (\n",
    "            char\n",
    "            .encode(\"unicode_escape\")\n",
    "            .decode()\n",
    "        )\n",
    "\n",
    "        column_name = (\n",
    "            f\"url_special_{safe_name}\"\n",
    "        )\n",
    "\n",
    "        temp[column_name] = (\n",
    "            temp[\"url\"]\n",
    "            .str.count(re.escape(char))\n",
    "        )\n",
    "\n",
    "    # ========================================================\n",
    "    # SUSPICIOUS PATTERN FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    combined = (\n",
    "        temp[\"url\"] + \" \" +\n",
    "        temp[\"content\"]\n",
    "    ).str.lower()\n",
    "\n",
    "    for feature, pattern in suspicious_patterns.items():\n",
    "\n",
    "        temp[feature] = combined.str.count(\n",
    "            pattern,\n",
    "            flags=re.IGNORECASE\n",
    "        )\n",
    "\n",
    "    # ========================================================\n",
    "    # ADDITIONAL NUMERICAL FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    # HTTP method encoding\n",
    "    method_mapping = {\n",
    "        \"GET\": 0,\n",
    "        \"POST\": 1,\n",
    "        \"PUT\": 2,\n",
    "        \"DELETE\": 3,\n",
    "        \"HEAD\": 4,\n",
    "        \"OPTIONS\": 5,\n",
    "        \"PATCH\": 6\n",
    "    }\n",
    "\n",
    "    temp[\"method_encoded\"] = (\n",
    "        temp[\"method\"]\n",
    "        .str.upper()\n",
    "        .map(method_mapping)\n",
    "        .fillna(-1)\n",
    "    )\n",
    "\n",
    "    # Combined request\n",
    "    request_combined = (\n",
    "        temp[\"method\"] + \" \" +\n",
    "        temp[\"url\"] + \" \" +\n",
    "        temp[\"content\"]\n",
    "    )\n",
    "\n",
    "    # Digit count\n",
    "    temp[\"digit_count\"] = request_combined.apply(\n",
    "        lambda x: sum(c.isdigit() for c in x)\n",
    "    )\n",
    "\n",
    "    # Special character count\n",
    "    temp[\"special_character_count\"] = request_combined.apply(\n",
    "        lambda x: sum(\n",
    "            not c.isalnum() and not c.isspace()\n",
    "            for c in x\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Uppercase count\n",
    "    temp[\"uppercase_count\"] = request_combined.apply(\n",
    "        lambda x: sum(c.isupper() for c in x)\n",
    "    )\n",
    "\n",
    "    # Request entropy\n",
    "    temp[\"request_entropy\"] = request_combined.apply(\n",
    "        calculate_entropy\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # VERIFY FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    missing_features = [\n",
    "        feature\n",
    "        for feature in numeric_features\n",
    "        if feature not in temp.columns\n",
    "    ]\n",
    "\n",
    "    if missing_features:\n",
    "\n",
    "        print(\"Missing numerical features:\")\n",
    "        print(missing_features)\n",
    "\n",
    "        raise ValueError(\n",
    "            \"Prediction cannot continue because \"\n",
    "            \"required numerical features are missing.\"\n",
    "        )\n",
    "\n",
    "    # ========================================================\n",
    "    # SELECT FEATURES IN EXACT TRAINING ORDER\n",
    "    # ========================================================\n",
    "\n",
    "    X_num_new = (\n",
    "        temp[numeric_features]\n",
    "        .fillna(0)\n",
    "        .astype(float)\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # SCALE NUMERICAL FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    X_num_scaled = scaler.transform(\n",
    "        X_num_new\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # CREATE REQUEST TEXT\n",
    "    # ========================================================\n",
    "\n",
    "    temp[\"request_text\"] = (\n",
    "        temp[\"method\"] + \" \" +\n",
    "        temp[\"url\"] + \" \" +\n",
    "        temp[\"content\"]\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # TF-IDF TRANSFORMATION\n",
    "    # ========================================================\n",
    "\n",
    "    X_text_new = tfidf.transform(\n",
    "        temp[\"request_text\"]\n",
    "    )\n",
    "\n",
    "    # ========================================================\n",
    "    # COMBINE TF-IDF + NUMERICAL FEATURES\n",
    "    # ========================================================\n",
    "\n",
    "    X_new = hstack([\n",
    "        X_text_new,\n",
    "        X_num_scaled\n",
    "    ]).tocsr()\n",
    "\n",
    "    # ========================================================\n",
    "    # CHECK FEATURE SIZE\n",
    "    # ========================================================\n",
    "\n",
    "    print(\"=\" * 60)\n",
    "    print(\"FEATURE INFORMATION\")\n",
    "    print(\"=\" * 60)\n",
    "\n",
    "    print(\n",
    "        f\"TF-IDF features: {X_text_new.shape[1]}\"\n",
    "    )\n",
    "\n",
    "    print(\n",
    "        f\"Numerical features: {X_num_scaled.shape[1]}\"\n",
    "    )\n",
    "\n",
    "    print(\n",
    "        f\"Total features: {X_new.shape[1]}\"\n",
    "    )\n",
    "\n",
    "    print(\"=\" * 60)\n",
    "\n",
    "    # ========================================================\n",
    "    # GET MODEL\n",
    "    # ========================================================\n",
    "\n",
    "    model = trained_models[model_name]\n",
    "\n",
    "    # ========================================================\n",
    "    # PREDICTION\n",
    "    # ========================================================\n",
    "\n",
    "    prediction = model.predict(\n",
    "        X_new\n",
    "    )[0]\n",
    "\n",
    "    prediction = int(prediction)\n",
    "\n",
    "    # ========================================================\n",
    "    # PROBABILITY\n",
    "    # ========================================================\n",
    "\n",
    "    probability = None\n",
    "\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "\n",
    "        probability = float(\n",
    "            model.predict_proba(X_new)[0, 1]\n",
    "        )\n",
    "\n",
    "    elif hasattr(model, \"decision_function\"):\n",
    "\n",
    "        decision_score = float(\n",
    "            model.decision_function(X_new)[0]\n",
    "        )\n",
    "\n",
    "        decision_score = np.clip(\n",
    "            decision_score,\n",
    "            -500,\n",
    "            500\n",
    "        )\n",
    "\n",
    "        probability = float(\n",
    "            1 / (1 + np.exp(-decision_score))\n",
    "        )\n",
    "\n",
    "    # ========================================================\n",
    "    # LABEL\n",
    "    # ========================================================\n",
    "\n",
    "    if prediction == 1:\n",
    "\n",
    "        label = \"Malicious\"\n",
    "\n",
    "    else:\n",
    "\n",
    "        label = \"Normal\"\n",
    "\n",
    "    # ========================================================\n",
    "    # DISPLAY RESULT\n",
    "    # ========================================================\n",
    "\n",
    "    print()\n",
    "    print(\"=\" * 60)\n",
    "    print(\"HTTP REQUEST PREDICTION\")\n",
    "    print(\"=\" * 60)\n",
    "\n",
    "    print(f\"Method: {method}\")\n",
    "    print(f\"URL: {url}\")\n",
    "    print(f\"Content: {content}\")\n",
    "    print(f\"Model: {model_name}\")\n",
    "\n",
    "    print(\"-\" * 60)\n",
    "\n",
    "    print(f\"Predicted class: {prediction}\")\n",
    "    print(f\"Prediction: {label}\")\n",
    "\n",
    "    if probability is not None:\n",
    "\n",
    "        print(\n",
    "            f\"Probability of malicious request: \"\n",
    "            f\"{probability:.2%}\"\n",
    "        )\n",
    "\n",
    "        print(\n",
    "            f\"Probability of normal request: \"\n",
    "            f\"{1 - probability:.2%}\"\n",
    "        )\n",
    "\n",
    "    print(\"=\" * 60)\n",
    "\n",
    "    # ========================================================\n",
    "    # RETURN RESULT\n",
    "    # ========================================================\n",
    "\n",
    "    return {\n",
    "    \"model\": model_name,\n",
    "    \"prediction\": prediction,\n",
    "    \"label\": label,\n",
    "    \"malicious_probability\": probability\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dd832a52-a68d-49d2-90d7-330138d3923e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n",
      "['url_length', 'content_length', 'url_parameter_count', 'url_special_<', 'url_special_>', \"url_special_'\", 'url_special_\"', 'url_special_;', 'url_special_=', 'url_special_&', 'url_special_%', 'url_special_/', 'url_special_\\\\\\\\', 'url_special_(', 'url_special_)', 'url_special_{', 'url_special_}', 'url_special_[', 'url_special_]', 'url_special_|', 'has_select', 'has_union', 'has_insert', 'has_update', 'has_delete', 'has_drop', 'has_script', 'has_alert', 'has_onerror', 'has_exec', 'has_cmd', 'has_shell', 'has_comment', 'has_encoded', 'method_encoded', 'digit_count', 'special_character_count', 'uppercase_count', 'request_entropy']\n"
     ]
    }
   ],
   "source": [
    "print(len(numeric_features))\n",
    "print(numeric_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8fcb59b4-d184-4a39-84f4-8648e9988692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "FEATURE INFORMATION\n",
      "============================================================\n",
      "TF-IDF features: 50000\n",
      "Numerical features: 39\n",
      "Total features: 50039\n",
      "============================================================\n",
      "\n",
      "============================================================\n",
      "HTTP REQUEST PREDICTION\n",
      "============================================================\n",
      "Method: GET\n",
      "URL: http://localhost:8080/tienda1/index.jsp\n",
      "Content: \n",
      "Model: SVM\n",
      "------------------------------------------------------------\n",
      "Predicted class: 0\n",
      "Prediction: Normal\n",
      "Probability of malicious request: 32.02%\n",
      "Probability of normal request: 67.98%\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "result = predict_http_request(\n",
    "    method=\"GET\",\n",
    "    url=\"http://localhost:8080/tienda1/index.jsp\",\n",
    "    content=\"\",\n",
    "    model_name=best_model_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c9ee4d-3308-4667-9a9d-902e431d33da",
   "metadata": {},
   "source": [
    "# An XSS-style request prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f69946eb-e6ce-4d35-b5f8-1ed2391f7df3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "FEATURE INFORMATION\n",
      "============================================================\n",
      "TF-IDF features: 50000\n",
      "Numerical features: 39\n",
      "Total features: 50039\n",
      "============================================================\n",
      "\n",
      "============================================================\n",
      "HTTP REQUEST PREDICTION\n",
      "============================================================\n",
      "Method: GET\n",
      "URL: http://localhost:8080/search.jsp?q=%3Cscript%3Ealert(1)%3C/script%3E\n",
      "Content: \n",
      "Model: SVM\n",
      "------------------------------------------------------------\n",
      "Predicted class: 1\n",
      "Prediction: Malicious\n",
      "Probability of malicious request: 99.47%\n",
      "Probability of normal request: 0.53%\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model': 'SVM',\n",
       " 'prediction': 1,\n",
       " 'label': 'Malicious',\n",
       " 'malicious_probability': 0.9946610890953201}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_http_request(\n",
    "    method=\"GET\",\n",
    "    url=\"http://localhost:8080/search.jsp?q=%3Cscript%3Ealert(1)%3C/script%3E\",\n",
    "    content=\"\",\n",
    "    model_name=best_model_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca3a60b-c195-4a14-8c42-b262ed2175bf",
   "metadata": {},
   "source": [
    "# Save the trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0ee6edc7-a955-4cc7-862d-2a9c22ee8d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All models and preprocessing objects saved successfully.\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save all tuned models\n",
    "for name, model in tuned_models.items():\n",
    "\n",
    "    filename = (\n",
    "        name.lower()\n",
    "        .replace(\" \", \"_\")\n",
    "        .replace(\"-\", \"_\")\n",
    "        + \".pkl\"\n",
    "    )\n",
    "\n",
    "    joblib.dump(\n",
    "        model,\n",
    "        filename\n",
    "    )\n",
    "\n",
    "# Save TF-IDF vectorizer\n",
    "joblib.dump(\n",
    "    tfidf,\n",
    "    \"tfidf_vectorizer.pkl\"\n",
    ")\n",
    "\n",
    "# Save feature scaler\n",
    "joblib.dump(\n",
    "    scaler,\n",
    "    \"feature_scaler.pkl\"\n",
    ")\n",
    "\n",
    "print(\"All models and preprocessing objects saved successfully.\")"
   ]
  }
 ],
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