{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvJjFDYMTXo6",
        "outputId": "bbc46499-751b-470a-d076-fad36a40638d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Note: you may need to restart the kernel to use updated packages.\n",
            "All libraries imported successfully!\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 1: IMPORT LIBRARIES\n",
        "# ===============================================\n",
        "\n",
        "%pip install -q xgboost lightgbm shap imbalanced-learn\n",
        "\n",
        "import os, random, warnings, glob, time\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Reproducibility\n",
        "RSEED = 42\n",
        "random.seed(RSEED)\n",
        "os.environ['PYTHONHASHSEED'] = str(RSEED)\n",
        "import numpy as np\n",
        "np.random.seed(RSEED)\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from xgboost import XGBClassifier # type: ignore\n",
        "import lightgbm as lgb # type: ignore\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "\n",
        "\n",
        "plt.style.use('seaborn-v0_8')\n",
        "sns.set_palette('husl')\n",
        "\n",
        "print(\"All libraries imported successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuhZNmUrTkjl",
        "outputId": "0d1a856c-c16f-4213-aed7-a8a4d85dd4bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Configuration parameters set successfully!\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 2: CONFIG PARAMS\n",
        "# ===============================================\n",
        "\n",
        "# Reproducibility seed\n",
        "RSEED = 42\n",
        "\n",
        "# Dataset path\n",
        "DATASET_PATH = r\"C:\\Users\\k.k\\Desktop\\hell on earth\\work\\UNSW_NB15\"\n",
        "\n",
        "# Model hyperparameters\n",
        "N_ESTIMATORS = 300\n",
        "LEARNING_RATE = 0.15\n",
        "MAX_DEPTH = 12\n",
        "NUM_LEAVES = 64\n",
        "SUBSAMPLE = 0.9\n",
        "COLSAMPLE_BYTREE = 0.9\n",
        "\n",
        "# Train-test split ratio\n",
        "TEST_SIZE = 0.2\n",
        "\n",
        "# SMOTE parameters\n",
        "SMOTE_RANDOM_STATE = 42\n",
        "\n",
        "# Save path for model\n",
        "SAVE_PATH = \"/content/drive/MyDrive/UNSW_NB15_Results\"\n",
        "\n",
        "print(\"Configuration parameters set successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "_IdpOjY1Tsyy",
        "outputId": "ebeeb1f4-92e3-45c5-face-521e57f293a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "--- Loading the UNSW-NB15 Dataset ---\n",
            "✓ Loaded UNSW-NB15_1.csv → (700001, 49)\n",
            "✓ Loaded UNSW-NB15_2.csv → (700001, 49)\n",
            "✓ Loaded UNSW-NB15_3.csv → (700001, 49)\n",
            "✓ Loaded UNSW-NB15_4.csv → (440044, 49)\n",
            "\n",
            "✓ Dataset loaded successfully!\n",
            "  - Shape: (2540047, 49)\n",
            "  - Columns: 49\n",
            "  - Memory usage: 949.57 MB\n"
          ]
        },
        {
          "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>srcip</th>\n",
              "      <th>sport</th>\n",
              "      <th>dstip</th>\n",
              "      <th>dsport</th>\n",
              "      <th>proto</th>\n",
              "      <th>state</th>\n",
              "      <th>dur</th>\n",
              "      <th>sbytes</th>\n",
              "      <th>dbytes</th>\n",
              "      <th>sttl</th>\n",
              "      <th>...</th>\n",
              "      <th>ct_ftp_cmd</th>\n",
              "      <th>ct_srv_src</th>\n",
              "      <th>ct_srv_dst</th>\n",
              "      <th>ct_dst_ltm</th>\n",
              "      <th>ct_src_ltm</th>\n",
              "      <th>ct_src_dport_ltm</th>\n",
              "      <th>ct_dst_sport_ltm</th>\n",
              "      <th>ct_dst_src_ltm</th>\n",
              "      <th>attack_cat</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59.166.0.0</td>\n",
              "      <td>1390</td>\n",
              "      <td>149.171.126.6</td>\n",
              "      <td>53</td>\n",
              "      <td>udp</td>\n",
              "      <td>CON</td>\n",
              "      <td>0.001055</td>\n",
              "      <td>132</td>\n",
              "      <td>164</td>\n",
              "      <td>31</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>59.166.0.0</td>\n",
              "      <td>33661</td>\n",
              "      <td>149.171.126.9</td>\n",
              "      <td>1024</td>\n",
              "      <td>udp</td>\n",
              "      <td>CON</td>\n",
              "      <td>0.036133</td>\n",
              "      <td>528</td>\n",
              "      <td>304</td>\n",
              "      <td>31</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>59.166.0.6</td>\n",
              "      <td>1464</td>\n",
              "      <td>149.171.126.7</td>\n",
              "      <td>53</td>\n",
              "      <td>udp</td>\n",
              "      <td>CON</td>\n",
              "      <td>0.001119</td>\n",
              "      <td>146</td>\n",
              "      <td>178</td>\n",
              "      <td>31</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59.166.0.5</td>\n",
              "      <td>3593</td>\n",
              "      <td>149.171.126.5</td>\n",
              "      <td>53</td>\n",
              "      <td>udp</td>\n",
              "      <td>CON</td>\n",
              "      <td>0.001209</td>\n",
              "      <td>132</td>\n",
              "      <td>164</td>\n",
              "      <td>31</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>59.166.0.3</td>\n",
              "      <td>49664</td>\n",
              "      <td>149.171.126.0</td>\n",
              "      <td>53</td>\n",
              "      <td>udp</td>\n",
              "      <td>CON</td>\n",
              "      <td>0.001169</td>\n",
              "      <td>146</td>\n",
              "      <td>178</td>\n",
              "      <td>31</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 49 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           srcip  sport          dstip dsport proto state       dur  sbytes  \\\n",
              "0  59.166.0.0   1390  149.171.126.6     53   udp   CON  0.001055     132   \n",
              "1     59.166.0.0  33661  149.171.126.9   1024   udp   CON  0.036133     528   \n",
              "2     59.166.0.6   1464  149.171.126.7     53   udp   CON  0.001119     146   \n",
              "3     59.166.0.5   3593  149.171.126.5     53   udp   CON  0.001209     132   \n",
              "4     59.166.0.3  49664  149.171.126.0     53   udp   CON  0.001169     146   \n",
              "\n",
              "   dbytes  sttl  ...  ct_ftp_cmd  ct_srv_src  ct_srv_dst ct_dst_ltm  \\\n",
              "0     164    31  ...           0           3           7          1   \n",
              "1     304    31  ...           0           2           4          2   \n",
              "2     178    31  ...           0          12           8          1   \n",
              "3     164    31  ...           0           6           9          1   \n",
              "4     178    31  ...           0           7           9          1   \n",
              "\n",
              "   ct_src_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  attack_cat  \\\n",
              "0           3                 1                 1               1         NaN   \n",
              "1           3                 1                 1               2         NaN   \n",
              "2           2                 2                 1               1         NaN   \n",
              "3           1                 1                 1               1         NaN   \n",
              "4           1                 1                 1               1         NaN   \n",
              "\n",
              "   label  \n",
              "0      0  \n",
              "1      0  \n",
              "2      0  \n",
              "3      0  \n",
              "4      0  \n",
              "\n",
              "[5 rows x 49 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 3: LOAD THE UNSW-NB15 DATASET (WITH COLUMNS)\n",
        "# ===============================================\n",
        "\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "print(\"\\n--- Loading the UNSW-NB15 Dataset ---\")\n",
        "\n",
        "\n",
        "# Column names (official UNSW-NB15 schema)\n",
        "columns = [\n",
        "    'srcip','sport','dstip','dsport','proto','state','dur','sbytes','dbytes','sttl','dttl',\n",
        "    'sloss','dloss','service','Sload','Dload','Spkts','Dpkts','swin','dwin','stcpb','dtcpb',\n",
        "    'smeansz','dmeansz','trans_depth','response_body_len','Sjit','Djit','Stime','Ltime',\n",
        "    'Sintpkt','Dintpkt','tcprtt','synack','ackdat','is_sm_ips_ports','ct_state_ttl',\n",
        "    'ct_flw_http_mthd','is_ftp_login','ct_ftp_cmd','ct_srv_src','ct_srv_dst','ct_dst_ltm',\n",
        "    'ct_src_ltm','ct_src_dport_ltm','ct_dst_sport_ltm','ct_dst_src_ltm','attack_cat','label'\n",
        "]\n",
        "\n",
        "df_list = []\n",
        "\n",
        "for i in range(1, 5):\n",
        "    file_path = os.path.join(DATASET_PATH, f\"UNSW-NB15_{i}.csv\")\n",
        "    \n",
        "    temp_df = pd.read_csv(\n",
        "        file_path,\n",
        "        header=None,          # IMPORTANT\n",
        "        names=columns,\n",
        "        encoding=\"latin-1\",\n",
        "        low_memory=False\n",
        "    )\n",
        "    \n",
        "    df_list.append(temp_df)\n",
        "    print(f\"✓ Loaded UNSW-NB15_{i}.csv → {temp_df.shape}\")\n",
        "\n",
        "df = pd.concat(df_list, ignore_index=True)\n",
        "\n",
        "# Basic dataset info\n",
        "print(\"\\n✓ Dataset loaded successfully!\")\n",
        "print(f\"  - Shape: {df.shape}\")\n",
        "print(f\"  - Columns: {len(df.columns)}\")\n",
        "print(f\"  - Memory usage: {df.memory_usage().sum() / 1024**2:.2f} MB\")\n",
        "\n",
        "# Preview\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x66wE2C9UKz4",
        "outputId": "6a4c2cb1-44e7-4813-e737-6443de1616d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting cleaning & feature selection...\n",
            "\n",
            "After cleaning → Shape: (2046753, 44)\n",
            "Encoded categorical columns: ['proto', 'service', 'state']\n",
            "Feature matrix: (2046753, 43)\n",
            "Selecting best features using ExtraTrees...\n",
            "Features reduced: 43 → 22\n",
            "\n",
            "CELL 4 COMPLETED SUCCESSFULLY ✅\n",
            "Created: X_selected, y, selected_features\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 4 — CLEANING & FEATURE SELECTION\n",
        "# ===============================================\n",
        "\n",
        "print(\"Starting cleaning & feature selection...\\n\")\n",
        "\n",
        "# -------------------------------\n",
        "# 1. BASIC CLEANING\n",
        "# -------------------------------\n",
        "df.columns = df.columns.str.strip()\n",
        "\n",
        "# Fix categorical text\n",
        "df['service'] = df['service'].replace('-', 'unknown').fillna('unknown')\n",
        "df['proto']   = df['proto'].fillna('unknown')\n",
        "df['state']   = df['state'].fillna('unknown')\n",
        "\n",
        "df['attack_cat'] = df['attack_cat'].astype(str).str.strip().fillna('Normal')\n",
        "\n",
        "# Drop identifiers & problematic columns\n",
        "drop_cols = ['srcip', 'dstip', 'sport', 'dsport', 'attack_cat']\n",
        "df.drop(columns=drop_cols, inplace=True)\n",
        "\n",
        "df.drop_duplicates(inplace=True)\n",
        "\n",
        "print(\"After cleaning → Shape:\", df.shape)\n",
        "\n",
        "# -------------------------------\n",
        "# 2. ENCODE CATEGORICAL FEATURES\n",
        "# -------------------------------\n",
        "cat_cols = ['proto', 'service', 'state']\n",
        "le = LabelEncoder()\n",
        "\n",
        "for col in cat_cols:\n",
        "    df[col] = le.fit_transform(df[col].astype(str))\n",
        "\n",
        "print(\"Encoded categorical columns:\", cat_cols)\n",
        "\n",
        "# -------------------------------\n",
        "# 3. FORCE NUMERIC (CRITICAL)\n",
        "# -------------------------------\n",
        "for col in df.columns:\n",
        "    if col != 'label':\n",
        "        df[col] = pd.to_numeric(df[col], errors='coerce')\n",
        "\n",
        "df.fillna(0, inplace=True)\n",
        "\n",
        "# -------------------------------\n",
        "# 4. FEATURES & TARGET\n",
        "# -------------------------------\n",
        "X = df.drop('label', axis=1)\n",
        "y = df['label'].astype(int)\n",
        "\n",
        "print(\"Feature matrix:\", X.shape)\n",
        "\n",
        "# -------------------------------\n",
        "# 5. FEATURE SELECTION (FAST & STRONG)\n",
        "# -------------------------------\n",
        "print(\"Selecting best features using ExtraTrees...\")\n",
        "\n",
        "selector_model = ExtraTreesClassifier(\n",
        "    n_estimators=100,\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "selector_model.fit(X, y)\n",
        "\n",
        "selector = SelectFromModel(\n",
        "    selector_model,\n",
        "    threshold='median',\n",
        "    prefit=True\n",
        ")\n",
        "\n",
        "X_selected = selector.transform(X)\n",
        "selected_features = X.columns[selector.get_support()].tolist()\n",
        "\n",
        "print(f\"Features reduced: {X.shape[1]} → {len(selected_features)}\")\n",
        "\n",
        "print(\"\\nCELL 4 COMPLETED SUCCESSFULLY ✅\")\n",
        "print(\"Created: X_selected, y, selected_features\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D5n3eP6-UQl1",
        "outputId": "86577437-f1e2-4eb6-d242-be57132f6f45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting Exploratory Data Analysis (EDA)...\n",
            "\n",
            "1. Class Distribution (label):\n",
            "label\n",
            "0    95.671388\n",
            "1     4.328612\n",
            "Name: proportion, dtype: float64\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EDA Complete! Key Insights: Severe imbalance (addressed by SMOTE); high correlations in flow stats; attacks have longer durations/higher TTL values.\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 5: EDA PROCESS\n",
        "# ===============================================\n",
        "\n",
        "print(\"Starting Exploratory Data Analysis (EDA)...\")\n",
        "\n",
        "# 1. Class Distribution (Critical for Imbalance Check)\n",
        "print(\"\\n1. Class Distribution (label):\")\n",
        "print(df['label'].value_counts(normalize=True) * 100)\n",
        "\n",
        "if 'attack_cat' in df.columns:\n",
        "    print(\"\\nAttack Categories Distribution:\")\n",
        "    print(df['attack_cat'].value_counts(normalize=True) * 100)\n",
        "\n",
        "# 2. Correlation Heatmap (Guides Feature Selection)\n",
        "num_cols = df.select_dtypes(include=np.number).columns\n",
        "corr = df[num_cols].corr().abs()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(corr, cmap='coolwarm', vmin=0, vmax=1)\n",
        "plt.title(\"Correlation Heatmap of Numerical Features\")\n",
        "plt.show()\n",
        "\n",
        "# 3. Distribution Plots for Key Features (e.g., dur, sttl – Shows Attack Patterns)\n",
        "key_features = ['dur', 'sttl']\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "for i, feature in enumerate(key_features):\n",
        "    sns.histplot(data=df, x=feature, hue='label', multiple='stack', ax=axes[i], log_scale=(True if feature == 'dur' else False))\n",
        "    axes[i].set_title(f\"Distribution of {feature} (Normal vs Attack)\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"EDA Complete! Key Insights: Severe imbalance (addressed by SMOTE); high correlations in flow stats; attacks have longer durations/higher TTL values.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "m-oxBQVfVVEq",
        "outputId": "9ee6aa91-c9e1-4d8c-cdce-25dbe3d14d8b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time-derived features created as lag alternatives!\n",
            "New features: ['dur_diff', 'sbytes_diff', 'dbytes_diff', 'time_diff', 'dur_rolling_mean', 'sbytes_rolling_mean']\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        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>proto</th>\n",
              "      <th>state</th>\n",
              "      <th>dur</th>\n",
              "      <th>sbytes</th>\n",
              "      <th>dbytes</th>\n",
              "      <th>sttl</th>\n",
              "      <th>dttl</th>\n",
              "      <th>sloss</th>\n",
              "      <th>dloss</th>\n",
              "      <th>service</th>\n",
              "      <th>...</th>\n",
              "      <th>ct_src_dport_ltm</th>\n",
              "      <th>ct_dst_sport_ltm</th>\n",
              "      <th>ct_dst_src_ltm</th>\n",
              "      <th>label</th>\n",
              "      <th>dur_diff</th>\n",
              "      <th>sbytes_diff</th>\n",
              "      <th>dbytes_diff</th>\n",
              "      <th>time_diff</th>\n",
              "      <th>dur_rolling_mean</th>\n",
              "      <th>sbytes_rolling_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>79</td>\n",
              "      <td>6</td>\n",
              "      <td>50.004341</td>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.004341</td>\n",
              "      <td>384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79</td>\n",
              "      <td>6</td>\n",
              "      <td>50.004337</td>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.004337</td>\n",
              "      <td>384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>13</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1780</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-50.004337</td>\n",
              "      <td>1396.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1780.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>120</td>\n",
              "      <td>2</td>\n",
              "      <td>0.001055</td>\n",
              "      <td>132</td>\n",
              "      <td>164</td>\n",
              "      <td>31</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001055</td>\n",
              "      <td>-1648.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.001055</td>\n",
              "      <td>132.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>114</td>\n",
              "      <td>5</td>\n",
              "      <td>34.275330</td>\n",
              "      <td>1684</td>\n",
              "      <td>10168</td>\n",
              "      <td>31</td>\n",
              "      <td>29</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>34.274275</td>\n",
              "      <td>1552.0</td>\n",
              "      <td>10004.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.857013</td>\n",
              "      <td>872.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 50 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   proto  state        dur  sbytes  dbytes  sttl  dttl  sloss  dloss  service  \\\n",
              "0     79      6  50.004341     384       0     1     0      0      0       12   \n",
              "1     79      6  50.004337     384       0     1     0      0      0       12   \n",
              "2     38     13   0.000000    1780       0    64     0      0      0       12   \n",
              "3    120      2   0.001055     132     164    31    29      0      0        1   \n",
              "4    114      5  34.275330    1684   10168    31    29      3      5        4   \n",
              "\n",
              "   ...  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  label   dur_diff  \\\n",
              "0  ...                 2                 4               2      0   0.000000   \n",
              "1  ...                 2                 4               2      0  -0.000004   \n",
              "2  ...                 1                 1               2      0 -50.004337   \n",
              "3  ...                 1                 1               1      0   0.001055   \n",
              "4  ...                 1                 1               1      0  34.274275   \n",
              "\n",
              "   sbytes_diff  dbytes_diff  time_diff  dur_rolling_mean  sbytes_rolling_mean  \n",
              "0          0.0          0.0        0.0         50.004341                384.0  \n",
              "1          0.0          0.0        0.0         50.004337                384.0  \n",
              "2       1396.0          0.0        4.0          0.000000               1780.0  \n",
              "3      -1648.0        164.0       33.0          0.001055                132.0  \n",
              "4       1552.0      10004.0        0.0         26.857013                872.8  \n",
              "\n",
              "[5 rows x 50 columns]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 6: LAG FEATURE\n",
        "# ===============================================\n",
        "\n",
        "# UNSW-NB15 is not strictly time-series, but we can create time-derived features as 'lag' alternatives\n",
        "# E.g., compute differences in timestamps, flow durations, or rolling stats on sorted data\n",
        "\n",
        "# Sort by timestamp for pseudo-time-series\n",
        "df = df.sort_values('Stime').reset_index(drop=True)\n",
        "\n",
        "# Create 'lag-like' features: differences from previous flow\n",
        "df['dur_diff'] = df['dur'].diff().fillna(0)  # Difference in duration from previous flow\n",
        "df['sbytes_diff'] = df['sbytes'].diff().fillna(0)  # Difference in source bytes\n",
        "df['dbytes_diff'] = df['dbytes'].diff().fillna(0)  # Difference in destination bytes\n",
        "df['time_diff'] = df['Stime'].diff().fillna(0)  # Time gap between flows\n",
        "\n",
        "# Rolling window features (e.g., mean over last 5 flows)\n",
        "window_size = 5\n",
        "df['dur_rolling_mean'] = df['dur'].rolling(window=window_size).mean().fillna(df['dur'])\n",
        "df['sbytes_rolling_mean'] = df['sbytes'].rolling(window=window_size).mean().fillna(df['sbytes'])\n",
        "\n",
        "print(\"Time-derived features created as lag alternatives!\")\n",
        "print(\"New features:\", ['dur_diff', 'sbytes_diff', 'dbytes_diff', 'time_diff', 'dur_rolling_mean', 'sbytes_rolling_mean'])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tyg9GkhJVaWm",
        "outputId": "d646f8a0-f310-4e10-ba8b-e511838f5924"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train set: (1637402, 28) (Normal: 1,566,525, Attack: 70,877)\n",
            "Test set: (409351, 28) (Normal: 391,632, Attack: 17,719)\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 7: TRAIN TEST SPLIT\n",
        "# ===============================================\n",
        "\n",
        "# Update X with new features from CELL 6\n",
        "new_features = ['dur_diff', 'sbytes_diff', 'dbytes_diff', 'time_diff', 'dur_rolling_mean', 'sbytes_rolling_mean']\n",
        "X = df[new_features + selected_features]  # Combine selected + new derived features\n",
        "y = df['label'].astype(int)\n",
        "\n",
        "# Train-test split (stratified to maintain class balance)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, stratify=y, random_state=RSEED)\n",
        "\n",
        "print(f\"Train set: {X_train.shape} (Normal: {sum(y_train==0):,}, Attack: {sum(y_train==1):,})\")\n",
        "print(f\"Test set: {X_test.shape} (Normal: {sum(y_test==0):,}, Attack: {sum(y_test==1):,})\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "U6_cIlrvZ53h"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training models...\n",
            "\n",
            "Training Random Forest...\n",
            "✓ RF Accuracy: 0.9931 | Time: 83.29s\n",
            "\n",
            "Training XGBoost...\n",
            "✓ XGB Accuracy: 0.9924 | Time: 11.68s\n",
            "\n",
            "Training LightGBM...\n",
            "[LightGBM] [Info] Number of positive: 70877, number of negative: 1566525\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.240712 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 4460\n",
            "[LightGBM] [Info] Number of data points in the train set: 1637402, number of used features: 28\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.043286 -> initscore=-3.095669\n",
            "[LightGBM] [Info] Start training from score -3.095669\n",
            "✓ LGBM Accuracy: 0.9930 | Time: 7.22s\n",
            "\n",
            "\n",
            "=== MODEL COMPARISON ===\n"
          ]
        },
        {
          "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>Training Time (seconds)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.993121</td>\n",
              "      <td>83.288817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.992368</td>\n",
              "      <td>11.678277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LightGBM</td>\n",
              "      <td>0.992972</td>\n",
              "      <td>7.221886</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Model  Accuracy  Training Time (seconds)\n",
              "0  Random Forest  0.993121                83.288817\n",
              "1        XGBoost  0.992368                11.678277\n",
              "2       LightGBM  0.992972                 7.221886"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 8: BUILD & TRAIN MODELS (RF, XGB, LGBM)\n",
        "# ===============================================\n",
        "\n",
        "import time\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "results = []\n",
        "\n",
        "print(\"Training models...\\n\")\n",
        "\n",
        "# =================================================\n",
        "# 1. RANDOM FOREST\n",
        "# =================================================\n",
        "print(\"Training Random Forest...\")\n",
        "\n",
        "rf_model = RandomForestClassifier(\n",
        "    n_estimators=100,\n",
        "    max_depth=15,\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "start = time.time()\n",
        "rf_model.fit(X_train, y_train)\n",
        "rf_time = time.time() - start\n",
        "\n",
        "rf_pred = rf_model.predict(X_test)\n",
        "rf_acc = accuracy_score(y_test, rf_pred)\n",
        "\n",
        "results.append([\"Random Forest\", rf_acc, rf_time])\n",
        "print(f\"✓ RF Accuracy: {rf_acc:.4f} | Time: {rf_time:.2f}s\\n\")\n",
        "\n",
        "\n",
        "# =================================================\n",
        "# 2. XGBOOST\n",
        "# =================================================\n",
        "print(\"Training XGBoost...\")\n",
        "\n",
        "xgb_model = XGBClassifier(\n",
        "    n_estimators=100,\n",
        "    max_depth=6,\n",
        "    learning_rate=0.1,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    tree_method=\"hist\",     # CPU safe\n",
        "    eval_metric=\"logloss\",\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "start = time.time()\n",
        "xgb_model.fit(X_train, y_train)\n",
        "xgb_time = time.time() - start\n",
        "\n",
        "xgb_pred = xgb_model.predict(X_test)\n",
        "xgb_acc = accuracy_score(y_test, xgb_pred)\n",
        "\n",
        "results.append([\"XGBoost\", xgb_acc, xgb_time])\n",
        "print(f\"✓ XGB Accuracy: {xgb_acc:.4f} | Time: {xgb_time:.2f}s\\n\")\n",
        "\n",
        "\n",
        "# =================================================\n",
        "# 3. LIGHTGBM\n",
        "# =================================================\n",
        "print(\"Training LightGBM...\")\n",
        "\n",
        "lgb_model = LGBMClassifier(\n",
        "    n_estimators=100,\n",
        "    learning_rate=0.1,\n",
        "    max_depth=-1,\n",
        "    num_leaves=31,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "start = time.time()\n",
        "lgb_model.fit(X_train, y_train)\n",
        "lgb_time = time.time() - start\n",
        "\n",
        "lgb_pred = lgb_model.predict(X_test)\n",
        "lgb_acc = accuracy_score(y_test, lgb_pred)\n",
        "\n",
        "results.append([\"LightGBM\", lgb_acc, lgb_time])\n",
        "print(f\"✓ LGBM Accuracy: {lgb_acc:.4f} | Time: {lgb_time:.2f}s\\n\")\n",
        "\n",
        "\n",
        "# =================================================\n",
        "# 4. SUMMARY TABLE\n",
        "# =================================================\n",
        "import pandas as pd\n",
        "\n",
        "results_df = pd.DataFrame(\n",
        "    results,\n",
        "    columns=[\"Model\", \"Accuracy\", \"Training Time (seconds)\"]\n",
        ")\n",
        "\n",
        "print(\"\\n=== MODEL COMPARISON ===\")\n",
        "results_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "eE42I6iRVkL2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluating Random Forest model...\n",
            "\n",
            "✓ Random Forest trained\n",
            "Accuracy : 0.9936\n",
            "ROC-AUC  : 0.9993\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Normal       1.00      1.00      1.00    391632\n",
            "      Attack       0.94      0.92      0.93     17719\n",
            "\n",
            "    accuracy                           0.99    409351\n",
            "   macro avg       0.97      0.96      0.96    409351\n",
            "weighted avg       0.99      0.99      0.99    409351\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✓ Evaluation complete\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 9: FINAL MODEL EVALUATION (SAFE & SIMPLE)\n",
        "# ===============================================\n",
        "\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    roc_auc_score,\n",
        "    roc_curve\n",
        ")\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "print(\"Evaluating Random Forest model...\\n\")\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 1. DEFINE & TRAIN RANDOM FOREST (AGAIN = SAFE)\n",
        "# ------------------------------------------------\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_model = RandomForestClassifier(\n",
        "    n_estimators=100,\n",
        "    max_depth=20,\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "print(\"✓ Random Forest trained\")\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 2. PREDICTIONS\n",
        "# ------------------------------------------------\n",
        "y_pred = rf_model.predict(X_test)\n",
        "y_prob = rf_model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 3. METRICS\n",
        "# ------------------------------------------------\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "roc = roc_auc_score(y_test, y_prob)\n",
        "\n",
        "print(f\"Accuracy : {acc:.4f}\")\n",
        "print(f\"ROC-AUC  : {roc:.4f}\\n\")\n",
        "\n",
        "print(\"Classification Report:\\n\")\n",
        "print(classification_report(y_test, y_pred, target_names=[\"Normal\", \"Attack\"]))\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 4. CONFUSION MATRIX\n",
        "# ------------------------------------------------\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(5, 4))\n",
        "sns.heatmap(\n",
        "    cm,\n",
        "    annot=True,\n",
        "    fmt=\"d\",\n",
        "    cmap=\"Blues\",\n",
        "    xticklabels=[\"Normal\", \"Attack\"],\n",
        "    yticklabels=[\"Normal\", \"Attack\"]\n",
        ")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Random Forest – Confusion Matrix\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 5. ROC CURVE\n",
        "# ------------------------------------------------\n",
        "fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
        "\n",
        "plt.figure(figsize=(5, 4))\n",
        "plt.plot(fpr, tpr, label=f\"AUC = {roc:.4f}\")\n",
        "plt.plot([0, 1], [0, 1], \"--\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"Random Forest – ROC Curve\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"✓ Evaluation complete\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "8lH2fxV6VmMW"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running predictions with Random Forest model...\n",
            "\n",
            "Single Sample Prediction:\n",
            "Predicted Class: Normal\n",
            "Attack Probability: 0.00000\n",
            "\n",
            "Batch Predictions (First 10 Test Samples):\n",
            "[0 0 1 0 0 0 0 0 0 0]\n",
            "\n",
            "✓ Model saved at: ./saved_model/random_forest_ids_model.pkl\n"
          ]
        }
      ],
      "source": [
        "# ===============================================\n",
        "# CELL 10: MODEL PREDICTION & SAVING (STABLE)\n",
        "# ===============================================\n",
        "\n",
        "import os\n",
        "import joblib\n",
        "\n",
        "print(\"Running predictions with Random Forest model...\\n\")\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 1. SINGLE SAMPLE PREDICTION\n",
        "# ------------------------------------------------\n",
        "new_data = X_test.iloc[[0]]  # keep as DataFrame\n",
        "\n",
        "pred_class = rf_model.predict(new_data)[0]\n",
        "pred_prob = rf_model.predict_proba(new_data)[0][1]\n",
        "\n",
        "print(\"Single Sample Prediction:\")\n",
        "print(f\"Predicted Class: {'Attack' if pred_class == 1 else 'Normal'}\")\n",
        "print(f\"Attack Probability: {pred_prob:.5f}\")\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 2. BATCH PREDICTION (FIRST 5)\n",
        "# ------------------------------------------------\n",
        "batch_preds = rf_model.predict(X_test.iloc[:10])\n",
        "\n",
        "print(\"\\nBatch Predictions (First 10 Test Samples):\")\n",
        "print(batch_preds)\n",
        "\n",
        "# ------------------------------------------------\n",
        "# 3. SAVE MODEL\n",
        "# ------------------------------------------------\n",
        "SAVE_PATH = \"./saved_model\"\n",
        "os.makedirs(SAVE_PATH, exist_ok=True)\n",
        "\n",
        "joblib.dump(rf_model, os.path.join(SAVE_PATH, \"random_forest_ids_model.pkl\"))\n",
        "\n",
        "print(f\"\\n✓ Model saved at: {SAVE_PATH}/random_forest_ids_model.pkl\")\n"
      ]
    }
  ],
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