Undersampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83c23234",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e2a6e70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled and replaced Bot_vs_BENIGN.csv\n",
      "Resampled and replaced DDoS_vs_BENIGN.csv\n",
      "Resampled and replaced DoS GoldenEye_vs_BENIGN.csv\n",
      "Resampled and replaced DoS Hulk_vs_BENIGN.csv\n",
      "Resampled and replaced DoS Slowhttptest_vs_BENIGN.csv\n",
      "Resampled and replaced DoS slowloris_vs_BENIGN.csv\n",
      "Resampled and replaced FTP-Patator_vs_BENIGN.csv\n",
      "Resampled and replaced Heartbleed_vs_BENIGN.csv\n",
      "Resampled and replaced Infiltration_vs_BENIGN.csv\n",
      "Resampled and replaced PortScan_vs_BENIGN.csv\n",
      "Resampled and replaced SSH-Patator_vs_BENIGN.csv\n",
      "Resampled and replaced Web Attack  Brute Force_vs_BENIGN.csv\n",
      "Resampled and replaced Web Attack  Sql Injection_vs_BENIGN.csv\n",
      "Resampled and replaced Web Attack  XSS_vs_BENIGN.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# List of attack types and BENIGN\n",
    "attack_types = [\"Bot\", \"DDoS\", \"DoS GoldenEye\", \"DoS Hulk\", \"DoS Slowhttptest\", \"DoS slowloris\", \"FTP-Patator\",\n",
    "                \"Heartbleed\", \"Infiltration\", \"PortScan\", \"SSH-Patator\", \"Web Attack  Brute Force\",\n",
    "                \"Web Attack  Sql Injection\", \"Web Attack  XSS\"]\n",
    "benign_type = \"BENIGN\"\n",
    "# Loop through each attack type\n",
    "for attack_type in attack_types:\n",
    "    # Load the combined data file\n",
    "    input_filename = f\"{attack_type}_vs_{benign_type}.csv\"\n",
    "    combined_data = pd.read_csv(input_filename)\n",
    "    \n",
    "    # Separate features (X) and labels (y)\n",
    "    X = combined_data.drop(columns=[\" Label\"])\n",
    "    y = combined_data[\" Label\"]\n",
    "    \n",
    "    # Skip undersampling if the class count is too low\n",
    "    if len(set(y)) <= 1:\n",
    "        print(f\"Skipping {attack_type}, not enough classes\")\n",
    "        continue\n",
    "    \n",
    "    # Perform undersampling using RandomUnderSampler\n",
    "    sampler = RandomUnderSampler(sampling_strategy=0.5, random_state=42)\n",
    "    X_resampled, y_resampled = sampler.fit_resample(X, y)\n",
    "    \n",
    "    # Create a DataFrame with resampled data\n",
    "    resampled_data = pd.DataFrame(X_resampled, columns=X.columns)\n",
    "    resampled_data[\"Label\"] = y_resampled\n",
    "    \n",
    "    # Save the resampled data back to the original file\n",
    "    resampled_data.to_csv(input_filename, index=False)\n",
    "    print(f\"Resampled and replaced {input_filename}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fde039d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyError",
     "evalue": "' Label'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ' Label'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m combined_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(input_filename)\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Count the number of instances for each class\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m class_counts \u001b[38;5;241m=\u001b[39m combined_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Label\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# Plot the class distribution\u001b[39;00m\n\u001b[1;32m     13\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: ' Label'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# Loop through each attack type\n",
    "for attack_type in attack_types:\n",
    "    # Load the combined data file\n",
    "    input_filename = f\"{attack_type}_vs_{benign_type}.csv\"\n",
    "    combined_data = pd.read_csv(input_filename)\n",
    "    \n",
    "    # Count the number of instances for each class\n",
    "    class_counts = combined_data[\" Label\"].value_counts()\n",
    "    \n",
    "    # Plot the class distribution\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    class_counts.plot(kind=\"bar\", color=[\"green\", \"red\"])\n",
    "    plt.title(f\"Class Distribution for {attack_type}\")\n",
    "    plt.xlabel(\"Class\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend([\"BENIGN\", attack_type])\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa7c1516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File: Bot_vs_BENIGN.csv\n",
      "Number of Benign instances: 3912\n",
      "Number of Attack instances: 1956\n",
      "Shape of the dataset: (5868, 85)\n",
      "-----------------------------\n",
      "File: DDoS_vs_BENIGN.csv\n",
      "Number of Benign instances: 256050\n",
      "Number of Attack instances: 128025\n",
      "Shape of the dataset: (384075, 85)\n",
      "-----------------------------\n",
      "File: DoS GoldenEye_vs_BENIGN.csv\n",
      "Number of Benign instances: 20586\n",
      "Number of Attack instances: 10293\n",
      "Shape of the dataset: (30879, 85)\n",
      "-----------------------------\n",
      "File: DoS Hulk_vs_BENIGN.csv\n",
      "Number of Benign instances: 460248\n",
      "Number of Attack instances: 230124\n",
      "Shape of the dataset: (690372, 85)\n",
      "-----------------------------\n",
      "File: DoS Slowhttptest_vs_BENIGN.csv\n",
      "Number of Benign instances: 10998\n",
      "Number of Attack instances: 5499\n",
      "Shape of the dataset: (16497, 85)\n",
      "-----------------------------\n",
      "File: DoS slowloris_vs_BENIGN.csv\n",
      "Number of Benign instances: 11592\n",
      "Number of Attack instances: 5796\n",
      "Shape of the dataset: (17388, 85)\n",
      "-----------------------------\n",
      "File: FTP-Patator_vs_BENIGN.csv\n",
      "Number of Benign instances: 15870\n",
      "Number of Attack instances: 7935\n",
      "Shape of the dataset: (23805, 85)\n",
      "-----------------------------\n",
      "File: Heartbleed_vs_BENIGN.csv\n",
      "Number of Benign instances: 22\n",
      "Number of Attack instances: 11\n",
      "Shape of the dataset: (33, 85)\n",
      "-----------------------------\n",
      "File: Infiltration_vs_BENIGN.csv\n",
      "Number of Benign instances: 72\n",
      "Number of Attack instances: 36\n",
      "Shape of the dataset: (108, 85)\n",
      "-----------------------------\n",
      "File: PortScan_vs_BENIGN.csv\n",
      "Number of Benign instances: 317608\n",
      "Number of Attack instances: 158804\n",
      "Shape of the dataset: (476412, 85)\n",
      "-----------------------------\n",
      "File: SSH-Patator_vs_BENIGN.csv\n",
      "Number of Benign instances: 11794\n",
      "Number of Attack instances: 5897\n",
      "Shape of the dataset: (17691, 85)\n",
      "-----------------------------\n",
      "File: Web Attack  Brute Force_vs_BENIGN.csv\n",
      "Number of Benign instances: 3014\n",
      "Number of Attack instances: 1507\n",
      "Shape of the dataset: (4521, 85)\n",
      "-----------------------------\n",
      "File: Web Attack  Sql Injection_vs_BENIGN.csv\n",
      "Number of Benign instances: 42\n",
      "Number of Attack instances: 21\n",
      "Shape of the dataset: (63, 85)\n",
      "-----------------------------\n",
      "File: Web Attack  XSS_vs_BENIGN.csv\n",
      "Number of Benign instances: 1304\n",
      "Number of Attack instances: 652\n",
      "Shape of the dataset: (1956, 85)\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "\n",
    "# List of file names\n",
    "file_names = [\n",
    "    'Bot_vs_BENIGN.csv', 'DDoS_vs_BENIGN.csv', 'DoS GoldenEye_vs_BENIGN.csv',\n",
    "    'DoS Hulk_vs_BENIGN.csv', 'DoS Slowhttptest_vs_BENIGN.csv',\n",
    "    'DoS slowloris_vs_BENIGN.csv', 'FTP-Patator_vs_BENIGN.csv',\n",
    "    'Heartbleed_vs_BENIGN.csv', 'Infiltration_vs_BENIGN.csv',\n",
    "    'PortScan_vs_BENIGN.csv', 'SSH-Patator_vs_BENIGN.csv',\n",
    "    'Web Attack  Brute Force_vs_BENIGN.csv',\n",
    "    'Web Attack  Sql Injection_vs_BENIGN.csv', 'Web Attack  XSS_vs_BENIGN.csv'\n",
    "]\n",
    "\n",
    "# Loop through each file\n",
    "for file_name in file_names:\n",
    "    # Read the file using pandas\n",
    "    data = pd.read_csv(file_name)\n",
    "    \n",
    "    # Count the number of benign and attack instances\n",
    "    num_benign = (data['Label'] == 'BENIGN').sum()\n",
    "    num_attack = (data['Label'] != 'BENIGN').sum()\n",
    "    \n",
    "    # Print information\n",
    "    print(f\"File: {file_name}\")\n",
    "    print(f\"Number of Benign instances: {num_benign}\")\n",
    "    print(f\"Number of Attack instances: {num_attack}\")\n",
    "    print(\"Shape of the dataset:\", data.shape)\n",
    "    print(\"-----------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c3f46da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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