CODE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "\n",
    "import pandas as pd\n",
    "from functools import reduce\n",
    "\n",
    "import ipaddress\n",
    "filename = \"ADA_Network.csv\"\n",
    "file1 = pd.read_csv(filename)\n",
    "file1.head(10)\n",
    "file1.isnull().sum\n",
    "\n",
    "# step-1 to replace all null\n",
    "update_file = file1.fillna(\" \")\n",
    "update_file.isnull().sum()\n",
    "\n",
    "update_file.to_csv('cleaned_'+filename, index = False)\n",
    "\n",
    "# step-2 to remove all rows with null value\n",
    "update_file = file1.fillna(0)\n",
    "\n",
    "# step-3 to convert tcp.flag, ip.dst, ip.src to integer\n",
    "update_file['tcp.flags'] = update_file['tcp.flags'].apply(lambda x: int(str(x), 16))\n",
    "\n",
    "update_file['ip.dst'] = update_file['ip.dst'].apply(lambda x: int(ipaddress.IPv4Address(x.split(\",\")[0].strip())) if isinstance(x, str) else x)\n",
    "update_file['ip.src'] = update_file['ip.src'].apply(lambda x: int(ipaddress.IPv4Address(x.split(\",\")[0].strip())) if isinstance(x, str) else x)\n",
    "\n",
    "update_file['ip.len'] = update_file['ip.len'].apply(lambda x: int(x.split(\",\")[0].strip() if isinstance(x, str) else x))\n",
    "update_file['ip.ttl'] = update_file['ip.ttl'].apply(lambda x: int(x.split(\",\")[0].strip() if isinstance(x, str) else x))\n",
    "\n",
    "update_file['udp.port'] = update_file['udp.port'].apply(lambda x: int(str(x).replace(',', '')))\n",
    "\n",
    "update_file['ip.flags.df'] = update_file['ip.flags.df'].apply(lambda x: 0 if x == 'Not set' else 1)\n",
    "update_file['ip.flags.mf'] = update_file['ip.flags.mf'].apply(lambda x: 0 if x == 'Not set' else 1)\n",
    "\n",
    "update_file['ip.proto'] = update_file['ip.proto'].apply(lambda x: 6 if x == 'TCP' else (17 if x == 'UDP' else 999))\n",
    "\n",
    "update_file['ip.fragment'] = update_file['ip.fragment'].apply(lambda x: int(x.replace(',', '')) if isinstance(x, str) else x)\n",
    "update_file['ip.fragments'] = update_file['ip.fragments'].apply(lambda x: 999 if isinstance(x, str) else x)\n",
    "\n",
    "# Function to replace non-ASCII characters with a specific number\n",
    "def replace_special_character(text, replacement_number):\n",
    "    if isinstance(text, str):\n",
    "        return text.replace('�', str(replacement_number))\n",
    "    else:\n",
    "        return text\n",
    "\n",
    "update_file['tcp.segments'] = update_file['tcp.segments'].apply(lambda x: replace_special_character(x, 9999))\n",
    "\n",
    "# Convert columns to numeric after replacing special characters\n",
    "update_file['tcp.segments'] = pd.to_numeric(update_file['tcp.segments'], errors='coerce')\n",
    "\n",
    "# Convert True/False to int\n",
    "update_file['http.request'] = update_file['http.request'].astype(int)\n",
    "\n",
    "update_file = update_file.fillna(0)\n",
    "update_file.to_csv('cleaned_'+filename, index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import csv\n",
    "\n",
    "file_name = sys.argv[1]\n",
    "label = sys.argv[2]\n",
    "\n",
    "file = open(file_name)\n",
    "content = csv.reader(file)\n",
    "row0 = next(content)\n",
    "row0.append('label')\n",
    "all = []\n",
    "all.append(row0)\n",
    "for item in content:\n",
    "    item.append(label)\n",
    "    all.append(item)\n",
    "\n",
    "new_file = open(label+'_'+ file_name, 'w')\n",
    "writer = csv.writer(new_file, lineterminator='\\n')\n",
    "writer.writerows(all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Master dataset has been created: master_dataset.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "data_directory = './'\n",
    "\n",
    "# # List of dataset files\n",
    "# dataset_files = [\n",
    "#     'benign_update_benign.csv',\n",
    "#     'bruteforce_update_bruteforce.csv',\n",
    "#     'ddos_update_ddos.csv',\n",
    "#     'probe_update_nmap.csv',\n",
    "#     'sqlattack_update_sqlattack.csv'\n",
    "# ]\n",
    "dataset_files = [\n",
    "    'bruteforce_cleaned_bruteforce.csv',\n",
    "    'ipdos_cleaned_ipdos.csv',\n",
    "    'sql_cleaned_sql.csv'\n",
    "]\n",
    "\n",
    "# Create an empty DataFrame to store the aggregated data\n",
    "master_dataset = pd.DataFrame()\n",
    "\n",
    "# Iterate through the dataset files and concatenate them\n",
    "for file in dataset_files:\n",
    "    file_path = os.path.join(data_directory, file)\n",
    "    df = pd.read_csv(file_path)\n",
    "    master_dataset = pd.concat([master_dataset, df], ignore_index=True)\n",
    "\n",
    "# Save the aggregated dataset to a new CSV file\n",
    "master_dataset.to_csv('master_dataset.csv', index=False)\n",
    "print(\"Master dataset has been created: master_dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ip.src</th>\n",
       "      <th>ip.dst</th>\n",
       "      <th>ip.len</th>\n",
       "      <th>ip.flags.df</th>\n",
       "      <th>ip.flags.mf</th>\n",
       "      <th>ip.fragment</th>\n",
       "      <th>ip.fragment.count</th>\n",
       "      <th>ip.fragments</th>\n",
       "      <th>ip.ttl</th>\n",
       "      <th>ip.proto</th>\n",
       "      <th>...</th>\n",
       "      <th>tcp.analysis.ack_rtt</th>\n",
       "      <th>tcp.segments</th>\n",
       "      <th>tcp.reassembled.length</th>\n",
       "      <th>http.request</th>\n",
       "      <th>udp.port</th>\n",
       "      <th>frame.time_relative</th>\n",
       "      <th>frame.time_delta</th>\n",
       "      <th>tcp.time_relative</th>\n",
       "      <th>tcp.time_delta</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>1.502095</td>\n",
       "      <td>0.752223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>bruteforce</td>\n",
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       "    <tr>\n",
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       "<p>73318 rows × 27 columns</p>\n",
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      ],
      "text/plain": [
       "          ip.src     ip.dst  ip.len  ip.flags.df  ip.flags.mf  ip.fragment  \\\n",
       "0      168430081  168430335    78.0            0            0          0.0   \n",
       "1      168430081  168430335    78.0            0            0          0.0   \n",
       "2      168430081  168430335    78.0            0            0          0.0   \n",
       "3      168430210  168430082    64.0            1            0          0.0   \n",
       "4      168430210  168430082    64.0            1            0          0.0   \n",
       "...          ...        ...     ...          ...          ...          ...   \n",
       "73313  168430081  168430210    47.0            1            0          0.0   \n",
       "73314  168430210  168430081    40.0            1            0          0.0   \n",
       "73315  168430210  168430081    43.0            1            0          0.0   \n",
       "73316  168430081  168430210    47.0            1            0          0.0   \n",
       "73317  168430210  168430081    40.0            1            0          0.0   \n",
       "\n",
       "       ip.fragment.count  ip.fragments  ip.ttl  ip.proto  ...  \\\n",
       "0                    0.0           0.0   128.0        17  ...   \n",
       "1                    0.0           0.0   128.0        17  ...   \n",
       "2                    0.0           0.0   128.0        17  ...   \n",
       "3                    0.0           0.0    64.0        17  ...   \n",
       "4                    0.0           0.0    64.0        17  ...   \n",
       "...                  ...           ...     ...       ...  ...   \n",
       "73313                0.0           0.0   128.0         6  ...   \n",
       "73314                0.0           0.0    64.0         6  ...   \n",
       "73315                0.0           0.0    64.0         6  ...   \n",
       "73316                0.0           0.0   128.0         6  ...   \n",
       "73317                0.0           0.0    64.0         6  ...   \n",
       "\n",
       "       tcp.analysis.ack_rtt  tcp.segments  tcp.reassembled.length  \\\n",
       "0                  0.000000           0.0                     0.0   \n",
       "1                  0.000000           0.0                     0.0   \n",
       "2                  0.000000           0.0                     0.0   \n",
       "3                  0.000000           0.0                     0.0   \n",
       "4                  0.000000           0.0                     0.0   \n",
       "...                     ...           ...                     ...   \n",
       "73313              0.002058           0.0                     0.0   \n",
       "73314              0.000036           0.0                     0.0   \n",
       "73315              0.000000           0.0                     0.0   \n",
       "73316              0.001657           0.0                     0.0   \n",
       "73317              0.000036           0.0                     0.0   \n",
       "\n",
       "       http.request  udp.port  frame.time_relative  frame.time_delta  \\\n",
       "0                 0    137137             0.000000          0.000000   \n",
       "1                 0    137137             0.749872          0.749872   \n",
       "2                 0    137137             1.502095          0.752223   \n",
       "3                 0   5297953             2.321336          0.819241   \n",
       "4                 0   5297953             2.322052          0.000717   \n",
       "...             ...       ...                  ...               ...   \n",
       "73313             0         0           840.934497          0.002058   \n",
       "73314             0         0           840.934533          0.000036   \n",
       "73315             0         0           865.938311         25.003779   \n",
       "73316             0         0           865.939968          0.001657   \n",
       "73317             0         0           865.940004          0.000036   \n",
       "\n",
       "       tcp.time_relative  tcp.time_delta       label  \n",
       "0               0.000000        0.000000  bruteforce  \n",
       "1               0.000000        0.000000  bruteforce  \n",
       "2               0.000000        0.000000  bruteforce  \n",
       "3               0.000000        0.000000  bruteforce  \n",
       "4               0.000000        0.000000  bruteforce  \n",
       "...                  ...             ...         ...  \n",
       "73313         275.343675        0.002058         sql  \n",
       "73314         275.343711        0.000036         sql  \n",
       "73315         300.347490       25.003779         sql  \n",
       "73316         300.349146        0.001657         sql  \n",
       "73317         300.349182        0.000036         sql  \n",
       "\n",
       "[73318 rows x 27 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data = pd.read_csv(\"master_dataset.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "bruteforce     5090\n",
       "ipdos         66818\n",
       "sql            1410\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('label').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "X = data[data.columns[:-1]]\n",
    "y = data[data.columns[-1]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9977904470934832"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "log_reg_model = Pipeline([\n",
    "    (\"imputer\", SimpleImputer()),\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"log_reg\", LogisticRegression(max_iter=1000))\n",
    "])\n",
    "\n",
    "log_reg_model.fit(X_train, y_train)\n",
    "log_reg_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9979087106746681"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp_model = Pipeline([\n",
    "    (\"imputer\", SimpleImputer()),\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"mlp\", MLPClassifier())\n",
    "])\n",
    "\n",
    "mlp_model.fit(X_train, y_train)\n",
    "mlp_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1500,     4,    23],\n",
       "       [    2, 20040,     4],\n",
       "       [   18,     4,   401]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_pred = log_reg_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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