CNN-LSTM_BinaryClassification.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## CNN-LSTM IoMT IDS\n",
    "### CSCI 6505 Group Project\n",
    "### Author: Hongwei Zhang & Koil Jat Chong\n",
    "### Enhancing Intrusion Detection in Healthcare IoMT Devices Using the CNN-LSTM Model"
   ],
   "id": "bb614ae877dbad0c"
  },
  {
   "cell_type": "code",
   "id": "9b9d35b7f709c8e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:30.506626Z",
     "start_time": "2024-12-01T21:11:29.176342Z"
    }
   },
   "source": [
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "print(\"Python version:\", sys.version)\n",
    "print(\"Version info:\", sys.version_info)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.10.11 (tags/v3.10.11:7d4cc5a, Apr  5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)]\n",
      "Version info: sys.version_info(major=3, minor=10, micro=11, releaselevel='final', serial=0)\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "1dc56bf228406df6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:30.538636Z",
     "start_time": "2024-12-01T21:11:30.524633Z"
    }
   },
   "source": [
    "# Read the dataset files list\n",
    "file_list = pd.read_csv('./dataset/file_list.csv')\n",
    "file_list.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                     File  Category              Attack  Class\n",
       "0             ARP_Spoofing_train.pcap.csv  SPOOFING        ARP Spoofing      1\n",
       "1                   Benign_train.pcap.csv    BENIGN              Benign      0\n",
       "2  MQTT-DDoS-Connect_Flood_train.pcap.csv      MQTT  DDoS Connect Flood      1\n",
       "3  MQTT-DDoS-Publish_Flood_train.pcap.csv      MQTT  DDoS Publish Flood      1\n",
       "4   MQTT-DoS-Connect_Flood_train.pcap.csv      MQTT   DoS Connect Flood      1"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File</th>\n",
       "      <th>Category</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARP_Spoofing_train.pcap.csv</td>\n",
       "      <td>SPOOFING</td>\n",
       "      <td>ARP Spoofing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Benign_train.pcap.csv</td>\n",
       "      <td>BENIGN</td>\n",
       "      <td>Benign</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MQTT-DDoS-Connect_Flood_train.pcap.csv</td>\n",
       "      <td>MQTT</td>\n",
       "      <td>DDoS Connect Flood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MQTT-DDoS-Publish_Flood_train.pcap.csv</td>\n",
       "      <td>MQTT</td>\n",
       "      <td>DDoS Publish Flood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MQTT-DoS-Connect_Flood_train.pcap.csv</td>\n",
       "      <td>MQTT</td>\n",
       "      <td>DoS Connect Flood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "7739ddbaaa83e9b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:43.621656Z",
     "start_time": "2024-12-01T21:11:30.698287Z"
    }
   },
   "source": [
    "# Import the dataset\n",
    "data_dir = \"./dataset/\"\n",
    "first_file_path = os.path.join(data_dir, file_list.iloc[0]['File'])\n",
    "column_names = pd.read_csv(first_file_path, nrows=0).columns.tolist()\n",
    "\n",
    "datasets_list = [] \n",
    "for _, row in file_list.iterrows():\n",
    "    file_path = os.path.join(data_dir, row['File'])\n",
    "    if os.path.exists(file_path):\n",
    "        data = pd.read_csv(file_path, header=None, skiprows=1, names=column_names)\n",
    "        data['Category'] = row['Category']\n",
    "        data['Attack'] = row['Attack']\n",
    "        data['Class'] = row['Class']\n",
    "        datasets_list.append(data)\n",
    "    else:\n",
    "        print(f\"File not found: {file_path}\")\n",
    "\n",
    "# Combine all datasets into a single DataFrame\n",
    "dataset = pd.concat(datasets_list, ignore_index=True)\n",
    "dataset"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         Header_Length  Protocol Type  Duration          Rate         Srate  \\\n",
       "0           866.600000           10.4      64.0  45722.390222  45722.390222   \n",
       "1          3934.300000           12.6     131.2  35708.799475  35708.799475   \n",
       "2          5592.800000           12.6      97.6     66.403506     66.403506   \n",
       "3          9303.600000           14.8      80.8     51.201280     51.201280   \n",
       "4          8592.400000           12.6      98.6     42.706455     42.706455   \n",
       "...                ...            ...       ...           ...           ...   \n",
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       "7160827   18136.530000           17.0      64.0  29318.535060  29318.535060   \n",
       "7160828   23038.040000           17.0      64.0  29363.556139  29363.556139   \n",
       "7160829   58526.160000           17.0      64.0  26168.955034  26168.955034   \n",
       "7160830  106790.104167           17.0      64.0  21326.211354  21326.211354   \n",
       "\n",
       "         Drate  fin_flag_number  syn_flag_number  rst_flag_number  \\\n",
       "0          0.0              0.0              0.0              0.0   \n",
       "1          0.0              0.0              0.0              0.0   \n",
       "2          0.0              0.0              0.0              0.0   \n",
       "3          0.0              0.0              0.0              0.0   \n",
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       "...        ...              ...              ...              ...   \n",
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       "7160830    0.0              0.0              0.0              0.0   \n",
       "\n",
       "         psh_flag_number  ...           IAT     Number   Magnitue      Radius  \\\n",
       "0                    0.3  ...  1.694026e+08   5.500000  16.963645  213.095221   \n",
       "1                    0.3  ...  1.694026e+08  13.500000  30.885371  708.919620   \n",
       "2                    0.3  ...  1.361110e-02   5.500000  21.787095  290.694475   \n",
       "3                    0.1  ...  1.694026e+08  13.500000  26.954506  597.046005   \n",
       "4                    0.2  ...  1.393099e-02   5.500000  24.255148  375.324132   \n",
       "...                  ...  ...           ...        ...        ...         ...   \n",
       "7160826              0.0  ...  8.467524e+07   9.500000  10.164175    8.453393   \n",
       "7160827              0.0  ...  8.467524e+07   9.500000  10.060924    3.012129   \n",
       "7160828              0.0  ...  8.467524e+07   9.500000  10.075262    3.287396   \n",
       "7160829              0.0  ...  8.467524e+07   9.500000  18.631072  205.641671   \n",
       "7160830              0.0  ...  7.056270e+07   9.333333  19.620028  115.038946   \n",
       "\n",
       "            Covariance  Variance      Weight  Category        Attack  Class  \n",
       "0         66236.076476  0.900000   38.500000  SPOOFING  ARP Spoofing      1  \n",
       "1        251721.126817  1.000000  244.600000  SPOOFING  ARP Spoofing      1  \n",
       "2         84028.647525  0.900000   38.500000  SPOOFING  ARP Spoofing      1  \n",
       "3        178453.001691  1.000000  244.600000  SPOOFING  ARP Spoofing      1  \n",
       "4         80115.110731  0.900000   38.500000  SPOOFING  ARP Spoofing      1  \n",
       "...                ...       ...         ...       ...           ...    ...  \n",
       "7160826     279.410488  0.130000  141.550000       DoS       DoS UDP      1  \n",
       "7160827      35.475423  0.130000  141.550000       DoS       DoS UDP      1  \n",
       "7160828      34.963050  0.160000  141.550000       DoS       DoS UDP      1  \n",
       "7160829   70932.249555  0.350000  141.550000       DoS       DoS UDP      1  \n",
       "7160830   38609.961724  0.177083  133.541667       DoS       DoS UDP      1  \n",
       "\n",
       "[7160831 rows x 48 columns]"
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       "      <th>syn_flag_number</th>\n",
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       "      <th>psh_flag_number</th>\n",
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       "      <th>IAT</th>\n",
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       "      <th>Covariance</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Weight</th>\n",
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       "      <th>0</th>\n",
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       "      <td>10.4</td>\n",
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       "      <td>1.694026e+08</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>16.963645</td>\n",
       "      <td>213.095221</td>\n",
       "      <td>66236.076476</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>SPOOFING</td>\n",
       "      <td>ARP Spoofing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3934.300000</td>\n",
       "      <td>12.6</td>\n",
       "      <td>131.2</td>\n",
       "      <td>35708.799475</td>\n",
       "      <td>35708.799475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
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       "      <td>1.694026e+08</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>30.885371</td>\n",
       "      <td>708.919620</td>\n",
       "      <td>251721.126817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244.600000</td>\n",
       "      <td>SPOOFING</td>\n",
       "      <td>ARP Spoofing</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5592.800000</td>\n",
       "      <td>12.6</td>\n",
       "      <td>97.6</td>\n",
       "      <td>66.403506</td>\n",
       "      <td>66.403506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.361110e-02</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>21.787095</td>\n",
       "      <td>290.694475</td>\n",
       "      <td>84028.647525</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>SPOOFING</td>\n",
       "      <td>ARP Spoofing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9303.600000</td>\n",
       "      <td>14.8</td>\n",
       "      <td>80.8</td>\n",
       "      <td>51.201280</td>\n",
       "      <td>51.201280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.694026e+08</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>26.954506</td>\n",
       "      <td>597.046005</td>\n",
       "      <td>178453.001691</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244.600000</td>\n",
       "      <td>SPOOFING</td>\n",
       "      <td>ARP Spoofing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8592.400000</td>\n",
       "      <td>12.6</td>\n",
       "      <td>98.6</td>\n",
       "      <td>42.706455</td>\n",
       "      <td>42.706455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.393099e-02</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>24.255148</td>\n",
       "      <td>375.324132</td>\n",
       "      <td>80115.110731</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>SPOOFING</td>\n",
       "      <td>ARP Spoofing</td>\n",
       "      <td>1</td>\n",
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       "      <td>9.500000</td>\n",
       "      <td>10.164175</td>\n",
       "      <td>8.453393</td>\n",
       "      <td>279.410488</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>141.550000</td>\n",
       "      <td>DoS</td>\n",
       "      <td>DoS UDP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7160827</th>\n",
       "      <td>18136.530000</td>\n",
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       "      <td>8.467524e+07</td>\n",
       "      <td>9.500000</td>\n",
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       "      <td>35.475423</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>141.550000</td>\n",
       "      <td>DoS</td>\n",
       "      <td>DoS UDP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "      <td>8.467524e+07</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>10.075262</td>\n",
       "      <td>3.287396</td>\n",
       "      <td>34.963050</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>141.550000</td>\n",
       "      <td>DoS</td>\n",
       "      <td>DoS UDP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7160829</th>\n",
       "      <td>58526.160000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>26168.955034</td>\n",
       "      <td>26168.955034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.467524e+07</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>18.631072</td>\n",
       "      <td>205.641671</td>\n",
       "      <td>70932.249555</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>141.550000</td>\n",
       "      <td>DoS</td>\n",
       "      <td>DoS UDP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7160830</th>\n",
       "      <td>106790.104167</td>\n",
       "      <td>17.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>21326.211354</td>\n",
       "      <td>21326.211354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.056270e+07</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>19.620028</td>\n",
       "      <td>115.038946</td>\n",
       "      <td>38609.961724</td>\n",
       "      <td>0.177083</td>\n",
       "      <td>133.541667</td>\n",
       "      <td>DoS</td>\n",
       "      <td>DoS UDP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7160831 rows × 48 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:43.761429Z",
     "start_time": "2024-12-01T21:11:43.741356Z"
    }
   },
   "cell_type": "code",
   "source": "dataset.info()",
   "id": "aefe1a9a8127ecd0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7160831 entries, 0 to 7160830\n",
      "Data columns (total 48 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   Header_Length    float64\n",
      " 1   Protocol Type    float64\n",
      " 2   Duration         float64\n",
      " 3   Rate             float64\n",
      " 4   Srate            float64\n",
      " 5   Drate            float64\n",
      " 6   fin_flag_number  float64\n",
      " 7   syn_flag_number  float64\n",
      " 8   rst_flag_number  float64\n",
      " 9   psh_flag_number  float64\n",
      " 10  ack_flag_number  float64\n",
      " 11  ece_flag_number  float64\n",
      " 12  cwr_flag_number  float64\n",
      " 13  ack_count        float64\n",
      " 14  syn_count        float64\n",
      " 15  fin_count        float64\n",
      " 16  rst_count        float64\n",
      " 17  HTTP             float64\n",
      " 18  HTTPS            float64\n",
      " 19  DNS              float64\n",
      " 20  Telnet           float64\n",
      " 21  SMTP             float64\n",
      " 22  SSH              float64\n",
      " 23  IRC              float64\n",
      " 24  TCP              float64\n",
      " 25  UDP              float64\n",
      " 26  DHCP             float64\n",
      " 27  ARP              float64\n",
      " 28  ICMP             float64\n",
      " 29  IGMP             float64\n",
      " 30  IPv              float64\n",
      " 31  LLC              float64\n",
      " 32  Tot sum          float64\n",
      " 33  Min              float64\n",
      " 34  Max              float64\n",
      " 35  AVG              float64\n",
      " 36  Std              float64\n",
      " 37  Tot size         float64\n",
      " 38  IAT              float64\n",
      " 39  Number           float64\n",
      " 40  Magnitue         float64\n",
      " 41  Radius           float64\n",
      " 42  Covariance       float64\n",
      " 43  Variance         float64\n",
      " 44  Weight           float64\n",
      " 45  Category         object \n",
      " 46  Attack           object \n",
      " 47  Class            int64  \n",
      "dtypes: float64(45), int64(1), object(2)\n",
      "memory usage: 2.6+ GB\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:50.994967Z",
     "start_time": "2024-12-01T21:11:43.858405Z"
    }
   },
   "cell_type": "code",
   "source": "dataset.describe()",
   "id": "2ea0477a1b9eabf2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Header_Length  Protocol Type      Duration          Rate         Srate  \\\n",
       "count   7.160831e+06   7.160831e+06  7.160831e+06  7.160831e+06  7.160831e+06   \n",
       "mean    2.958836e+04   8.042957e+00  6.474653e+01  1.523202e+04  1.523202e+04   \n",
       "std     2.762618e+05   6.292201e+00  8.405262e+00  3.950968e+04  3.950968e+04   \n",
       "min     0.000000e+00   0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%     5.400000e+01   1.160000e+00  6.400000e+01  6.685014e+00  6.685014e+00   \n",
       "50%     1.080000e+02   6.000000e+00  6.400000e+01  1.452887e+02  1.452887e+02   \n",
       "75%     1.958050e+04   1.700000e+01  6.400000e+01  1.914681e+04  1.914681e+04   \n",
       "max     9.895636e+06   1.700000e+01  2.550000e+02  2.097152e+06  2.097152e+06   \n",
       "\n",
       "           Drate  fin_flag_number  syn_flag_number  rst_flag_number  \\\n",
       "count  7160831.0     7.160831e+06     7.160831e+06     7.160831e+06   \n",
       "mean         0.0     5.079549e-03     1.587609e-01     3.862139e-02   \n",
       "std          0.0     3.411170e-02     3.388233e-01     1.371016e-01   \n",
       "min          0.0     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "25%          0.0     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "50%          0.0     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "75%          0.0     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "max          0.0     1.000000e+00     1.000000e+00     1.000000e+00   \n",
       "\n",
       "       psh_flag_number  ...           Std      Tot size           IAT  \\\n",
       "count     7.160831e+06  ...  7.160831e+06  7.160831e+06  7.160831e+06   \n",
       "mean      2.240655e-02  ...  6.405743e+00  6.084036e+01  8.468349e+07   \n",
       "std       9.713147e-02  ...  3.917360e+01  8.787628e+01  1.788442e+07   \n",
       "min       0.000000e+00  ...  0.000000e+00  4.200000e+01 -1.282061e+00   \n",
       "25%       0.000000e+00  ...  0.000000e+00  4.258000e+01  8.467888e+07   \n",
       "50%       0.000000e+00  ...  0.000000e+00  5.000000e+01  8.469641e+07   \n",
       "75%       0.000000e+00  ...  0.000000e+00  5.400000e+01  8.469686e+07   \n",
       "max       1.000000e+00  ...  7.211509e+02  1.514000e+03  1.694694e+08   \n",
       "\n",
       "             Number      Magnitue        Radius    Covariance      Variance  \\\n",
       "count  7.160831e+06  7.160831e+06  7.160831e+06  7.160831e+06  7.160831e+06   \n",
       "mean   9.499081e+00  1.045008e+01  9.047367e+00  2.535226e+03  9.148464e-02   \n",
       "std    8.446717e-01  3.169197e+00  5.538149e+01  2.045832e+04  2.327133e-01   \n",
       "min    1.000000e+00  9.165151e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    9.500000e+00  9.199377e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    9.500000e+00  1.000000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    9.500000e+00  1.039230e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max    1.500000e+01  5.502727e+01  1.020232e+03  5.204379e+05  1.000000e+00   \n",
       "\n",
       "             Weight         Class  \n",
       "count  7.160831e+06  7.160831e+06  \n",
       "mean   1.415273e+02  9.730852e-01  \n",
       "std    2.174138e+01  1.618343e-01  \n",
       "min    1.000000e+00  0.000000e+00  \n",
       "25%    1.415500e+02  1.000000e+00  \n",
       "50%    1.415500e+02  1.000000e+00  \n",
       "75%    1.415500e+02  1.000000e+00  \n",
       "max    2.446000e+02  1.000000e+00  \n",
       "\n",
       "[8 rows x 46 columns]"
      ],
      "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>Header_Length</th>\n",
       "      <th>Protocol Type</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Srate</th>\n",
       "      <th>Drate</th>\n",
       "      <th>fin_flag_number</th>\n",
       "      <th>syn_flag_number</th>\n",
       "      <th>rst_flag_number</th>\n",
       "      <th>psh_flag_number</th>\n",
       "      <th>...</th>\n",
       "      <th>Std</th>\n",
       "      <th>Tot size</th>\n",
       "      <th>IAT</th>\n",
       "      <th>Number</th>\n",
       "      <th>Magnitue</th>\n",
       "      <th>Radius</th>\n",
       "      <th>Covariance</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7160831.0</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "      <td>7.160831e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.958836e+04</td>\n",
       "      <td>8.042957e+00</td>\n",
       "      <td>6.474653e+01</td>\n",
       "      <td>1.523202e+04</td>\n",
       "      <td>1.523202e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.079549e-03</td>\n",
       "      <td>1.587609e-01</td>\n",
       "      <td>3.862139e-02</td>\n",
       "      <td>2.240655e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>6.405743e+00</td>\n",
       "      <td>6.084036e+01</td>\n",
       "      <td>8.468349e+07</td>\n",
       "      <td>9.499081e+00</td>\n",
       "      <td>1.045008e+01</td>\n",
       "      <td>9.047367e+00</td>\n",
       "      <td>2.535226e+03</td>\n",
       "      <td>9.148464e-02</td>\n",
       "      <td>1.415273e+02</td>\n",
       "      <td>9.730852e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.762618e+05</td>\n",
       "      <td>6.292201e+00</td>\n",
       "      <td>8.405262e+00</td>\n",
       "      <td>3.950968e+04</td>\n",
       "      <td>3.950968e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.411170e-02</td>\n",
       "      <td>3.388233e-01</td>\n",
       "      <td>1.371016e-01</td>\n",
       "      <td>9.713147e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>3.917360e+01</td>\n",
       "      <td>8.787628e+01</td>\n",
       "      <td>1.788442e+07</td>\n",
       "      <td>8.446717e-01</td>\n",
       "      <td>3.169197e+00</td>\n",
       "      <td>5.538149e+01</td>\n",
       "      <td>2.045832e+04</td>\n",
       "      <td>2.327133e-01</td>\n",
       "      <td>2.174138e+01</td>\n",
       "      <td>1.618343e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>-1.282061e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.165151e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>1.160000e+00</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>6.685014e+00</td>\n",
       "      <td>6.685014e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.258000e+01</td>\n",
       "      <td>8.467888e+07</td>\n",
       "      <td>9.500000e+00</td>\n",
       "      <td>9.199377e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.415500e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.080000e+02</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>1.452887e+02</td>\n",
       "      <td>1.452887e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>8.469641e+07</td>\n",
       "      <td>9.500000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.415500e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.958050e+04</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>1.914681e+04</td>\n",
       "      <td>1.914681e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>8.469686e+07</td>\n",
       "      <td>9.500000e+00</td>\n",
       "      <td>1.039230e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.415500e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.895636e+06</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>2.550000e+02</td>\n",
       "      <td>2.097152e+06</td>\n",
       "      <td>2.097152e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.211509e+02</td>\n",
       "      <td>1.514000e+03</td>\n",
       "      <td>1.694694e+08</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>5.502727e+01</td>\n",
       "      <td>1.020232e+03</td>\n",
       "      <td>5.204379e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.446000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:51.166668Z",
     "start_time": "2024-12-01T21:11:51.026285Z"
    }
   },
   "cell_type": "code",
   "source": "dataset['Category'].value_counts()",
   "id": "27644f6acc9745fc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "DDoS        4779859\n",
       "DoS         1805529\n",
       "MQTT         262938\n",
       "BENIGN       192732\n",
       "RECON        103726\n",
       "SPOOFING      16047\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:51.336170Z",
     "start_time": "2024-12-01T21:11:51.201419Z"
    }
   },
   "cell_type": "code",
   "source": "dataset['Attack'].value_counts()",
   "id": "1ea3a4fece8209de",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attack\n",
       "DDoS UDP              1635956\n",
       "DDoS ICMP             1537476\n",
       "DDoS TCP               804465\n",
       "DDoS SYN               801962\n",
       "DoS UDP                566950\n",
       "DoS SYN                441903\n",
       "DoS ICMP               416292\n",
       "DoS TCP                380384\n",
       "Benign                 192732\n",
       "DDoS Connect Flood     173036\n",
       "Port Scan               83981\n",
       "DoS Publish Flood       44376\n",
       "DDoS Publish Flood      27623\n",
       "OS Scan                 16832\n",
       "ARP Spoofing            16047\n",
       "DoS Connect Flood       12773\n",
       "Malformed Data           5130\n",
       "VulScan                  2173\n",
       "Ping Sweep                740\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:51.433584Z",
     "start_time": "2024-12-01T21:11:51.402060Z"
    }
   },
   "cell_type": "code",
   "source": "dataset['Class'].value_counts()",
   "id": "f0c1b0365944af12",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "1    6968099\n",
       "0     192732\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:52.588760Z",
     "start_time": "2024-12-01T21:11:51.492529Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Select 'Category' to perform multiclass classification\n",
    "selected_data = pd.concat([dataset.iloc[:, :45], dataset.iloc[:, -1]], axis=1)\n",
    "selected_data.head()"
   ],
   "id": "c43d497415a45747",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Header_Length  Protocol Type  Duration          Rate         Srate  Drate  \\\n",
       "0          866.6           10.4      64.0  45722.390222  45722.390222    0.0   \n",
       "1         3934.3           12.6     131.2  35708.799475  35708.799475    0.0   \n",
       "2         5592.8           12.6      97.6     66.403506     66.403506    0.0   \n",
       "3         9303.6           14.8      80.8     51.201280     51.201280    0.0   \n",
       "4         8592.4           12.6      98.6     42.706455     42.706455    0.0   \n",
       "\n",
       "   fin_flag_number  syn_flag_number  rst_flag_number  psh_flag_number  ...  \\\n",
       "0              0.0              0.0              0.0              0.3  ...   \n",
       "1              0.0              0.0              0.0              0.3  ...   \n",
       "2              0.0              0.0              0.0              0.3  ...   \n",
       "3              0.0              0.0              0.0              0.1  ...   \n",
       "4              0.0              0.0              0.0              0.2  ...   \n",
       "\n",
       "          Std  Tot size           IAT  Number   Magnitue      Radius  \\\n",
       "0  150.681076     431.8  1.694026e+08     5.5  16.963645  213.095221   \n",
       "1  500.702909     406.3  1.694026e+08    13.5  30.885371  708.919620   \n",
       "2  205.552035     386.6  1.361110e-02     5.5  21.787095  290.694475   \n",
       "3  421.683660     300.2  1.694026e+08    13.5  26.954506  597.046005   \n",
       "4  265.394239     209.2  1.393099e-02     5.5  24.255148  375.324132   \n",
       "\n",
       "      Covariance  Variance  Weight  Class  \n",
       "0   66236.076476       0.9    38.5      1  \n",
       "1  251721.126817       1.0   244.6      1  \n",
       "2   84028.647525       0.9    38.5      1  \n",
       "3  178453.001691       1.0   244.6      1  \n",
       "4   80115.110731       0.9    38.5      1  \n",
       "\n",
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       "      <td>205.552035</td>\n",
       "      <td>386.6</td>\n",
       "      <td>1.361110e-02</td>\n",
       "      <td>5.5</td>\n",
       "      <td>21.787095</td>\n",
       "      <td>290.694475</td>\n",
       "      <td>84028.647525</td>\n",
       "      <td>0.9</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>209.2</td>\n",
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       "<p>5 rows × 46 columns</p>\n",
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:55.801515Z",
     "start_time": "2024-12-01T21:11:52.658896Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Under sampling\n",
    "rus = RandomUnderSampler(random_state=0)\n",
    "X, y = rus.fit_resample(selected_data.iloc[:, :-1], selected_data.iloc[:, -1])\n",
    "processed_data = pd.DataFrame(X)\n",
    "processed_data['Class'] = y\n",
    "processed_data.head()"
   ],
   "id": "c53c50ef57b735d3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Header_Length  Protocol Type  Duration          Rate         Srate  \\\n",
       "16047          134.3            7.1      64.0  40415.288318  40415.288318   \n",
       "16048          137.0            4.7      49.9  58749.191833  58749.191833   \n",
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       "16051          572.3            7.1      57.7      2.549120      2.549120   \n",
       "\n",
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       "16047    0.0              0.0              0.0              0.0   \n",
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       "16050    0.0              0.0              0.0              0.0   \n",
       "16051    0.0              0.0              0.0              0.0   \n",
       "\n",
       "       psh_flag_number  ...        Std  Tot size           IAT  Number  \\\n",
       "16047              0.5  ...  48.225852      95.3  1.694641e+08     5.5   \n",
       "16048              0.2  ...  59.753361     125.6  1.694641e+08    13.5   \n",
       "16049              0.5  ...  10.223690      80.6  8.070469e-05     5.5   \n",
       "16050              0.5  ...  15.625023      78.5  1.694641e+08    13.5   \n",
       "16051              0.4  ...  69.738867     139.0  9.993820e-02     5.5   \n",
       "\n",
       "        Magnitue     Radius    Covariance  Variance  Weight  Class  \n",
       "16047  15.312231  68.201654   2644.242675       0.9    38.5      0  \n",
       "16048  13.468364  84.997147   5328.483794       1.0   244.6      0  \n",
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:55.895673Z",
     "start_time": "2024-12-01T21:11:55.880043Z"
    }
   },
   "cell_type": "code",
   "source": "processed_data['Class'].value_counts()",
   "id": "f633784da83aa1a6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    192732\n",
       "1    192732\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:56.194317Z",
     "start_time": "2024-12-01T21:11:56.099818Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.title(\"Label Distribution\")\n",
    "processed_data.groupby('Class').size().plot(kind='pie', autopct='%.2f', figsize=(8,8))"
   ],
   "id": "135385a7c4a5a3eb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Label Distribution'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:56.496419Z",
     "start_time": "2024-12-01T21:11:56.273368Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Normalize the data\n",
    "scaler = MinMaxScaler()\n",
    "normalize_data = processed_data.columns[:-1]\n",
    "processed_data[normalize_data] = scaler.fit_transform(processed_data[normalize_data])\n",
    "processed_data.head()"
   ],
   "id": "4941429e9f7ac5fa",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Header_Length  Protocol Type  Duration      Rate     Srate  Drate  \\\n",
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:56.752830Z",
     "start_time": "2024-12-01T21:11:56.528144Z"
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   "cell_type": "code",
   "source": [
    "# Shuffle the dataset\n",
    "processed_data = processed_data.sample(frac=1).reset_index(drop=True)\n",
    "processed_data.head()"
   ],
   "id": "cb4ff8b1d5b42e",
   "outputs": [
    {
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       "\n",
       "        Std  Tot size           IAT    Number  Magnitue    Radius  \\\n",
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     "execution_count": 14,
     "metadata": {},
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   "execution_count": 14
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   },
   "cell_type": "code",
   "source": "processed_data.info()",
   "id": "bfc9d38f4e6092b2",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 385464 entries, 0 to 385463\n",
      "Data columns (total 46 columns):\n",
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      "---  ------           --------------   -----  \n",
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      " 11  ece_flag_number  385464 non-null  float64\n",
      " 12  cwr_flag_number  385464 non-null  float64\n",
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      " 14  syn_count        385464 non-null  float64\n",
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      " 17  HTTP             385464 non-null  float64\n",
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      " 32  Tot sum          385464 non-null  float64\n",
      " 33  Min              385464 non-null  float64\n",
      " 34  Max              385464 non-null  float64\n",
      " 35  AVG              385464 non-null  float64\n",
      " 36  Std              385464 non-null  float64\n",
      " 37  Tot size         385464 non-null  float64\n",
      " 38  IAT              385464 non-null  float64\n",
      " 39  Number           385464 non-null  float64\n",
      " 40  Magnitue         385464 non-null  float64\n",
      " 41  Radius           385464 non-null  float64\n",
      " 42  Covariance       385464 non-null  float64\n",
      " 43  Variance         385464 non-null  float64\n",
      " 44  Weight           385464 non-null  float64\n",
      " 45  Class            385464 non-null  int64  \n",
      "dtypes: float64(45), int64(1)\n",
      "memory usage: 135.3 MB\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:57.327754Z",
     "start_time": "2024-12-01T21:11:56.917348Z"
    }
   },
   "cell_type": "code",
   "source": "processed_data.describe()",
   "id": "c9ce0474fb4cb5a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Header_Length  Protocol Type       Duration          Rate  \\\n",
       "count  385464.000000  385464.000000  385464.000000  3.854640e+05   \n",
       "mean        0.022057       0.434710       0.269198  3.775875e-03   \n",
       "std         0.078325       0.282073       0.096961  1.417722e-02   \n",
       "min         0.000000       0.000000       0.000000  0.000000e+00   \n",
       "25%         0.000011       0.352941       0.250980  6.965165e-07   \n",
       "50%         0.002939       0.352941       0.250980  1.899352e-06   \n",
       "75%         0.017468       0.417647       0.250980  1.027334e-04   \n",
       "max         1.000000       1.000000       1.000000  1.000000e+00   \n",
       "\n",
       "              Srate     Drate  fin_flag_number  syn_flag_number  \\\n",
       "count  3.854640e+05  385464.0    385464.000000    385464.000000   \n",
       "mean   3.775875e-03       0.0         0.003715         0.082243   \n",
       "std    1.417722e-02       0.0         0.027907         0.254873   \n",
       "min    0.000000e+00       0.0         0.000000         0.000000   \n",
       "25%    6.965165e-07       0.0         0.000000         0.000000   \n",
       "50%    1.899352e-06       0.0         0.000000         0.000000   \n",
       "75%    1.027334e-04       0.0         0.000000         0.000000   \n",
       "max    1.000000e+00       0.0         1.000000         1.000000   \n",
       "\n",
       "       rst_flag_number  psh_flag_number  ...            Std       Tot size  \\\n",
       "count    385464.000000    385464.000000  ...  385464.000000  385464.000000   \n",
       "mean          0.020546         0.213653  ...       0.046871       0.054639   \n",
       "std           0.101169         0.246775  ...       0.116337       0.166278   \n",
       "min           0.000000         0.000000  ...       0.000000       0.000000   \n",
       "25%           0.000000         0.000000  ...       0.000000       0.005435   \n",
       "50%           0.000000         0.000000  ...       0.002091       0.016304   \n",
       "75%           0.000000         0.500000  ...       0.025201       0.026562   \n",
       "max           1.000000         1.000000  ...       1.000000       1.000000   \n",
       "\n",
       "                IAT         Number       Magnitue         Radius  \\\n",
       "count  3.854640e+05  385464.000000  385464.000000  385464.000000   \n",
       "mean   4.999316e-01       0.607103       0.091445       0.046807   \n",
       "std    3.567174e-01       0.203867       0.174282       0.116356   \n",
       "min    0.000000e+00       0.000000       0.000000       0.000000   \n",
       "25%    8.156635e-09       0.321429       0.018203       0.000000   \n",
       "50%    4.997740e-01       0.607143       0.050673       0.002022   \n",
       "75%    9.999532e-01       0.892857       0.082017       0.024994   \n",
       "max    1.000000e+00       1.000000       1.000000       1.000000   \n",
       "\n",
       "          Covariance       Variance         Weight          Class  \n",
       "count  385464.000000  385464.000000  385464.000000  385464.000000  \n",
       "mean        0.018807       0.464998       0.576917       0.500000  \n",
       "std         0.078124       0.459216       0.301812       0.500001  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.153941       0.000000  \n",
       "50%         0.000024       0.260000       0.576970       0.500000  \n",
       "75%         0.000782       0.900000       1.000000       1.000000  \n",
       "max         1.000000       1.000000       1.000000       1.000000  \n",
       "\n",
       "[8 rows x 46 columns]"
      ],
      "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>Header_Length</th>\n",
       "      <th>Protocol Type</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Srate</th>\n",
       "      <th>Drate</th>\n",
       "      <th>fin_flag_number</th>\n",
       "      <th>syn_flag_number</th>\n",
       "      <th>rst_flag_number</th>\n",
       "      <th>psh_flag_number</th>\n",
       "      <th>...</th>\n",
       "      <th>Std</th>\n",
       "      <th>Tot size</th>\n",
       "      <th>IAT</th>\n",
       "      <th>Number</th>\n",
       "      <th>Magnitue</th>\n",
       "      <th>Radius</th>\n",
       "      <th>Covariance</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>3.854640e+05</td>\n",
       "      <td>3.854640e+05</td>\n",
       "      <td>385464.0</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>3.854640e+05</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "      <td>385464.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.022057</td>\n",
       "      <td>0.434710</td>\n",
       "      <td>0.269198</td>\n",
       "      <td>3.775875e-03</td>\n",
       "      <td>3.775875e-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003715</td>\n",
       "      <td>0.082243</td>\n",
       "      <td>0.020546</td>\n",
       "      <td>0.213653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046871</td>\n",
       "      <td>0.054639</td>\n",
       "      <td>4.999316e-01</td>\n",
       "      <td>0.607103</td>\n",
       "      <td>0.091445</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.464998</td>\n",
       "      <td>0.576917</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.078325</td>\n",
       "      <td>0.282073</td>\n",
       "      <td>0.096961</td>\n",
       "      <td>1.417722e-02</td>\n",
       "      <td>1.417722e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027907</td>\n",
       "      <td>0.254873</td>\n",
       "      <td>0.101169</td>\n",
       "      <td>0.246775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116337</td>\n",
       "      <td>0.166278</td>\n",
       "      <td>3.567174e-01</td>\n",
       "      <td>0.203867</td>\n",
       "      <td>0.174282</td>\n",
       "      <td>0.116356</td>\n",
       "      <td>0.078124</td>\n",
       "      <td>0.459216</td>\n",
       "      <td>0.301812</td>\n",
       "      <td>0.500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>6.965165e-07</td>\n",
       "      <td>6.965165e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>8.156635e-09</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>0.018203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153941</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.002939</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>1.899352e-06</td>\n",
       "      <td>1.899352e-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.016304</td>\n",
       "      <td>4.997740e-01</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.050673</td>\n",
       "      <td>0.002022</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.576970</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.017468</td>\n",
       "      <td>0.417647</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>1.027334e-04</td>\n",
       "      <td>1.027334e-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025201</td>\n",
       "      <td>0.026562</td>\n",
       "      <td>9.999532e-01</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.082017</td>\n",
       "      <td>0.024994</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Model Creation and Training",
   "id": "81113f2f8e3aa41"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:57.390264Z",
     "start_time": "2024-12-01T21:11:57.359043Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Extract features and labels\n",
    "X = processed_data.drop(columns=['Class']).values\n",
    "y = processed_data['Class'].values\n",
    "print(f'X: {X.shape}, y: {y.shape}')"
   ],
   "id": "f76c86dca877b07a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: (385464, 45), y: (385464,)\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:57.791819Z",
     "start_time": "2024-12-01T21:11:57.578798Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Split the data into train, validation, and test sets\n",
    "# 70% training, 15% validation, 15% testing\n",
    "X_train, X_rest, y_train, y_rest = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_rest, y_rest, test_size=0.5, random_state=42)\n",
    "print('Train set:', X_train.shape, y_train.shape)\n",
    "print('Validation set:', X_val.shape, y_val.shape)\n",
    "print('Test set:', X_test.shape, y_test.shape)"
   ],
   "id": "c1c959e69b4c3969",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (269824, 45) (269824,)\n",
      "Validation set: (57820, 45) (57820,)\n",
      "Test set: (57820, 45) (57820,)\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:57.851930Z",
     "start_time": "2024-12-01T21:11:57.821943Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Convert to PyTorch tensors\n",
    "X_train = torch.tensor(X_train, dtype=torch.float32)\n",
    "y_train = torch.tensor(y_train, dtype=torch.int64)\n",
    "X_val = torch.tensor(X_val, dtype=torch.float32)\n",
    "y_val = torch.tensor(y_val, dtype=torch.int64)\n",
    "X_test = torch.tensor(X_test, dtype=torch.float32)\n",
    "y_test = torch.tensor(y_test, dtype=torch.int64)\n",
    "\n",
    "# Create data loaders\n",
    "batch_size = 128\n",
    "train_loader = DataLoader(TensorDataset(X_train, y_train),\n",
    "                          batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(X_val, y_val), batch_size=batch_size)\n",
    "test_loader = DataLoader(TensorDataset(X_test, y_test), batch_size=batch_size)"
   ],
   "id": "a8000b6f82143af2",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:11:57.891900Z",
     "start_time": "2024-12-01T21:11:57.871931Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Define the model\n",
    "class CNN_LSTM_Model(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
    "        super(CNN_LSTM_Model, self).__init__()\n",
    "\n",
    "        # CNN layers for spatial feature extraction\n",
    "        self.cnn = nn.Sequential(\n",
    "            nn.Conv1d(in_channels=input_size, out_channels=64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(kernel_size=2, stride=2),\n",
    "            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool1d(kernel_size=2, stride=2)\n",
    "        )\n",
    "\n",
    "        # LSTM layers for temporal feature extraction\n",
    "        self.lstm = nn.LSTM(\n",
    "            input_size=128,\n",
    "            hidden_size=hidden_size,\n",
    "            num_layers=num_layers,\n",
    "            batch_first=True,\n",
    "            dropout=0.2\n",
    "        )\n",
    "\n",
    "        # Fully connected layer for classification\n",
    "        self.fc = nn.Linear(hidden_size, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if x.dim() == 2: \n",
    "            x = x.unsqueeze(2)\n",
    "        x = x.permute(0, 2, 1)\n",
    "        out = self.cnn(x)\n",
    "        out = out.permute(0, 2, 1)\n",
    "        out, _ = self.lstm(out)\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out"
   ],
   "id": "fa9351966ec570c6",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:12:00.410272Z",
     "start_time": "2024-12-01T21:11:59.438549Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Initialize the model, loss function, and optimizer\n",
    "input_size = 1\n",
    "hidden_size = 256\n",
    "num_layers = 2\n",
    "num_classes = 2\n",
    "\n",
    "model = CNN_LSTM_Model(input_size, hidden_size, num_layers, num_classes).to('cpu')\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "print(model)"
   ],
   "id": "70b7430587346486",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN_LSTM_Model(\n",
      "  (cnn): Sequential(\n",
      "    (0): Conv1d(1, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
      "    (4): ReLU()\n",
      "    (5): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (lstm): LSTM(128, 256, num_layers=2, batch_first=True, dropout=0.2)\n",
      "  (fc): Linear(in_features=256, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:12:01.632428Z",
     "start_time": "2024-12-01T21:12:01.616222Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Training function\n",
    "def train(model, dataloader, criterion, optimizer):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "\n",
    "    for inputs, labels in dataloader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "\n",
    "    return running_loss / len(dataloader.dataset)\n",
    "\n",
    "# Evaluation function\n",
    "def evaluate(model, dataloader, criterion):\n",
    "    model.eval()\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in dataloader:\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            running_loss += loss.item() * inputs.size(0)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            correct += (preds == labels).sum().item()\n",
    "            all_preds.extend(preds.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "    # Compute additional metrics\n",
    "    accuracy = correct / len(dataloader.dataset)\n",
    "    precision = precision_score(all_labels, all_preds, average='macro')\n",
    "    recall = recall_score(all_labels, all_preds, average='macro')\n",
    "    f1 = f1_score(all_labels, all_preds, average='macro')\n",
    "\n",
    "    return running_loss / len(dataloader.dataset), accuracy, precision, recall, f1"
   ],
   "id": "a58ffad3c454f54b",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:46:38.905986Z",
     "start_time": "2024-12-01T21:12:02.783316Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Train and evaluate the model\n",
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    train_loss = train(model, train_loader, criterion, optimizer)\n",
    "    val_loss, val_acc, val_prec, val_rec, val_f1 = evaluate(model, val_loader, criterion)\n",
    "    print(\n",
    "        f\"Epoch {epoch + 1}/{num_epochs}: <Train Loss: {train_loss:.6f}, Val Loss: {val_loss:.6f}, Val Acc: {val_acc:.6f}, Val Prec: {val_prec:.6f}, Val Rec: {val_rec:.6f}, Val F1: {val_f1:.6f}>\")"
   ],
   "id": "1af59232d38035c7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10: <Train Loss: 0.030262, Val Loss: 0.013013, Val Acc: 0.997561, Val Prec: 0.997573, Val Rec: 0.997561, Val F1: 0.997561>\n",
      "Epoch 2/10: <Train Loss: 0.012328, Val Loss: 0.012802, Val Acc: 0.997423, Val Prec: 0.997436, Val Rec: 0.997422, Val F1: 0.997423>\n",
      "Epoch 3/10: <Train Loss: 0.011863, Val Loss: 0.012322, Val Acc: 0.997527, Val Prec: 0.997539, Val Rec: 0.997526, Val F1: 0.997527>\n",
      "Epoch 4/10: <Train Loss: 0.011343, Val Loss: 0.012061, Val Acc: 0.997769, Val Prec: 0.997780, Val Rec: 0.997768, Val F1: 0.997769>\n",
      "Epoch 5/10: <Train Loss: 0.010938, Val Loss: 0.011056, Val Acc: 0.997734, Val Prec: 0.997744, Val Rec: 0.997734, Val F1: 0.997734>\n",
      "Epoch 6/10: <Train Loss: 0.010456, Val Loss: 0.010663, Val Acc: 0.997838, Val Prec: 0.997848, Val Rec: 0.997837, Val F1: 0.997838>\n",
      "Epoch 7/10: <Train Loss: 0.010210, Val Loss: 0.011039, Val Acc: 0.997821, Val Prec: 0.997830, Val Rec: 0.997820, Val F1: 0.997821>\n",
      "Epoch 8/10: <Train Loss: 0.010083, Val Loss: 0.010300, Val Acc: 0.997752, Val Prec: 0.997762, Val Rec: 0.997751, Val F1: 0.997752>\n",
      "Epoch 9/10: <Train Loss: 0.009828, Val Loss: 0.010516, Val Acc: 0.997821, Val Prec: 0.997830, Val Rec: 0.997820, Val F1: 0.997821>\n",
      "Epoch 10/10: <Train Loss: 0.009603, Val Loss: 0.010235, Val Acc: 0.997855, Val Prec: 0.997865, Val Rec: 0.997855, Val F1: 0.997855>\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-01T21:46:46.466171Z",
     "start_time": "2024-12-01T21:46:41.549224Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Evaluate on the test set\n",
    "loss, accuracy, precision, recall, f1 = evaluate(model, test_loader, criterion)\n",
    "print(f'Test Loss: {loss:.6f}'\n",
    "      f'\\nAccuracy: {accuracy:.6f}'\n",
    "      f'\\nPrecision: {precision:.6f}'\n",
    "      f'\\nRecall: {recall:.6f}'\n",
    "      f'\\nF1-score: {f1:.6f}')"
   ],
   "id": "7a5eec2bbbd6016a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.009629\n",
      "Accuracy: 0.997873\n",
      "Precision: 0.997869\n",
      "Recall: 0.997885\n",
      "F1-score: 0.997873\n"
     ]
    }
   ],
   "execution_count": 24
  }
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