RF-MLP_CICIoMT2024_19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb4816ad-0795-42ee-88a7-664cb59a7615",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import river.metrics as metrics\n",
    "import river.stream as stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ed28cdc-a331-4cde-acc0-14afee0367b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install deep-river\n",
    "#!pip install prettytable\n",
    "#!pip install tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29ac9f76-7832-42a2-b344-bf5d247d5b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.utils import shuffle\n",
    "from torch import nn, manual_seed\n",
    "from IPython.display import clear_output\n",
    "from collections import deque, Counter\n",
    "from river import metrics, preprocessing, compose, stream\n",
    "from deep_river import classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e68c660d-eb0d-4f6c-a86f-eb434e5db2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "offline_df = pd.read_csv('CICIoMT2024/Train.csv')\n",
    "offline_df = offline_df.sample(frac=0.01, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08c1b816-2bb7-4779-a5a3-4c57a0e8f365",
   "metadata": {},
   "outputs": [],
   "source": [
    "online_df = pd.read_csv('CICIoMT2024/Test.csv')\n",
    "online_df = online_df.sample(frac=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9316b058-aabe-4eaa-84fa-8142456cf041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 71557 entries, 6612853 to 3840050\n",
      "Data columns (total 46 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Header_Length    71557 non-null  float64\n",
      " 1   Protocol Type    71557 non-null  float64\n",
      " 2   Duration         71557 non-null  float64\n",
      " 3   Rate             71557 non-null  float64\n",
      " 4   Srate            71557 non-null  float64\n",
      " 5   Drate            71557 non-null  float64\n",
      " 6   fin_flag_number  71557 non-null  float64\n",
      " 7   syn_flag_number  71557 non-null  float64\n",
      " 8   rst_flag_number  71557 non-null  float64\n",
      " 9   psh_flag_number  71557 non-null  float64\n",
      " 10  ack_flag_number  71557 non-null  float64\n",
      " 11  ece_flag_number  71557 non-null  float64\n",
      " 12  cwr_flag_number  71557 non-null  float64\n",
      " 13  ack_count        71557 non-null  float64\n",
      " 14  syn_count        71557 non-null  float64\n",
      " 15  fin_count        71557 non-null  float64\n",
      " 16  rst_count        71557 non-null  float64\n",
      " 17  HTTP             71557 non-null  float64\n",
      " 18  HTTPS            71557 non-null  float64\n",
      " 19  DNS              71557 non-null  float64\n",
      " 20  Telnet           71557 non-null  float64\n",
      " 21  SMTP             71557 non-null  float64\n",
      " 22  SSH              71557 non-null  float64\n",
      " 23  IRC              71557 non-null  float64\n",
      " 24  TCP              71557 non-null  float64\n",
      " 25  UDP              71557 non-null  float64\n",
      " 26  DHCP             71557 non-null  float64\n",
      " 27  ARP              71557 non-null  float64\n",
      " 28  ICMP             71557 non-null  float64\n",
      " 29  IGMP             71557 non-null  float64\n",
      " 30  IPv              71557 non-null  float64\n",
      " 31  LLC              71557 non-null  float64\n",
      " 32  Tot sum          71557 non-null  float64\n",
      " 33  Min              71557 non-null  float64\n",
      " 34  Max              71557 non-null  float64\n",
      " 35  AVG              71557 non-null  float64\n",
      " 36  Std              71557 non-null  float64\n",
      " 37  Tot size         71557 non-null  float64\n",
      " 38  IAT              71557 non-null  float64\n",
      " 39  Number           71557 non-null  float64\n",
      " 40  Magnitue         71557 non-null  float64\n",
      " 41  Radius           71557 non-null  float64\n",
      " 42  Covariance       71557 non-null  float64\n",
      " 43  Variance         71557 non-null  float64\n",
      " 44  Weight           71557 non-null  float64\n",
      " 45  Label            71557 non-null  object \n",
      "dtypes: float64(45), object(1)\n",
      "memory usage: 25.7+ MB\n"
     ]
    }
   ],
   "source": [
    "offline_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66b72d17-49ce-4f82-9b19-a4ca372bb794",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 161212 entries, 1088358 to 315349\n",
      "Data columns (total 46 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   Header_Length    161212 non-null  float64\n",
      " 1   Protocol Type    161212 non-null  float64\n",
      " 2   Duration         161212 non-null  float64\n",
      " 3   Rate             161212 non-null  float64\n",
      " 4   Srate            161212 non-null  float64\n",
      " 5   Drate            161212 non-null  float64\n",
      " 6   fin_flag_number  161212 non-null  float64\n",
      " 7   syn_flag_number  161212 non-null  float64\n",
      " 8   rst_flag_number  161212 non-null  float64\n",
      " 9   psh_flag_number  161212 non-null  float64\n",
      " 10  ack_flag_number  161212 non-null  float64\n",
      " 11  ece_flag_number  161212 non-null  float64\n",
      " 12  cwr_flag_number  161212 non-null  float64\n",
      " 13  ack_count        161212 non-null  float64\n",
      " 14  syn_count        161212 non-null  float64\n",
      " 15  fin_count        161212 non-null  float64\n",
      " 16  rst_count        161212 non-null  float64\n",
      " 17  HTTP             161212 non-null  float64\n",
      " 18  HTTPS            161212 non-null  float64\n",
      " 19  DNS              161212 non-null  float64\n",
      " 20  Telnet           161212 non-null  float64\n",
      " 21  SMTP             161212 non-null  float64\n",
      " 22  SSH              161212 non-null  float64\n",
      " 23  IRC              161212 non-null  float64\n",
      " 24  TCP              161212 non-null  float64\n",
      " 25  UDP              161212 non-null  float64\n",
      " 26  DHCP             161212 non-null  float64\n",
      " 27  ARP              161212 non-null  float64\n",
      " 28  ICMP             161212 non-null  float64\n",
      " 29  IGMP             161212 non-null  float64\n",
      " 30  IPv              161212 non-null  float64\n",
      " 31  LLC              161212 non-null  float64\n",
      " 32  Tot sum          161212 non-null  float64\n",
      " 33  Min              161212 non-null  float64\n",
      " 34  Max              161212 non-null  float64\n",
      " 35  AVG              161212 non-null  float64\n",
      " 36  Std              161212 non-null  float64\n",
      " 37  Tot size         161212 non-null  float64\n",
      " 38  IAT              161212 non-null  float64\n",
      " 39  Number           161212 non-null  float64\n",
      " 40  Magnitue         161212 non-null  float64\n",
      " 41  Radius           161212 non-null  float64\n",
      " 42  Covariance       161212 non-null  float64\n",
      " 43  Variance         161212 non-null  float64\n",
      " 44  Weight           161212 non-null  float64\n",
      " 45  Label            161212 non-null  object \n",
      "dtypes: float64(45), object(1)\n",
      "memory usage: 57.8+ MB\n"
     ]
    }
   ],
   "source": [
    "online_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea8e57c1-357f-41f5-970e-73d8da5153fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\n",
      "DDoS-UDP              16235\n",
      "DDoS-ICMP             15475\n",
      "DDoS-SYN               8134\n",
      "DDoS-TCP               8008\n",
      "DoS-UDP                5660\n",
      "DoS-SYN                4408\n",
      "DoS-ICMP               4189\n",
      "DoS-TCP                3810\n",
      "Benign                 1914\n",
      "DDoS-Connect_Flood     1707\n",
      "Port_Scan               747\n",
      "DoS-Publish_Flood       441\n",
      "DDoS-Publish_Flood      300\n",
      "ARP_Spoofing            165\n",
      "OS_Scan                 163\n",
      "DoS-Connect_Flood       129\n",
      "Malformed_Data           48\n",
      "VulScan                  18\n",
      "Ping_Sweep                6\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(offline_df['Label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e24d32ea-fb6d-46bf-acb7-68c16c1e316d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\n",
      "DDoS-UDP              36400\n",
      "DDoS-ICMP             34910\n",
      "DDoS-TCP              18381\n",
      "DDoS-SYN              17231\n",
      "DoS-UDP               13711\n",
      "DoS-SYN                9881\n",
      "DoS-ICMP               9781\n",
      "DoS-TCP                8086\n",
      "DDoS-Connect_Flood     4109\n",
      "Benign                 3789\n",
      "Port_Scan              2129\n",
      "DoS-Publish_Flood       896\n",
      "DDoS-Publish_Flood      815\n",
      "OS_Scan                 341\n",
      "DoS-Connect_Flood       282\n",
      "Malformed_Data          185\n",
      "ARP_Spoofing            167\n",
      "VulScan                  89\n",
      "Ping_Sweep               29\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(online_df['Label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a28e3656-cf67-450e-9d9b-bff386639012",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dictionary of Types\n",
    "attacks = {'DDoS-UDP':0, \n",
    "           'DDoS-ICMP':1,\n",
    "           'DDoS-TCP':2,\n",
    "           'DDoS-SYN':3,\n",
    "           'DoS-UDP':4,\n",
    "           'DoS-SYN':5, \n",
    "           'DoS-ICMP':6, \n",
    "           'DoS-TCP':7, \n",
    "           'DDoS-Connect_Flood':8,           \n",
    "           'Benign':9,\n",
    "           'Port_Scan':10,\n",
    "           'DoS-Publish_Flood':11, \n",
    "           'DDoS-Publish_Flood':12,\n",
    "           'OS_Scan':13,\n",
    "           'DoS-Connect_Flood':14, \n",
    "           'Malformed_Data':15, \n",
    "           'ARP_Spoofing':16,\n",
    "           'VulScan':17, \n",
    "           'Ping_Sweep':18}\n",
    "\n",
    "offline_df['Label'] = offline_df['Label'].map(attacks)\n",
    "online_df['Label'] = online_df['Label'].map(attacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592b2a75-857e-4f4c-a643-f666d35fb940",
   "metadata": {},
   "source": [
    "# Train Offline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d552cc7d-449e-4570-bf7b-b67be2603989",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = offline_df.drop(columns=['Label'])\n",
    "y_train = offline_df['Label']\n",
    "X_train, y_train = shuffle(X_train, y_train, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4a90418-8620-4ebc-8d73-cb81e3a15c59",
   "metadata": {},
   "outputs": [
    {
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       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
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       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
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       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
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       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
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       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
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       "  background-color: var(--sklearn-color-background);\n",
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       "#sk-container-id-1 div.sk-parallel-item {\n",
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       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
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       "#sk-container-id-1 div.sk-toggleable {\n",
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       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Offline training setup\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "offline_model = RandomForestClassifier()\n",
    "offline_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af488923-00d6-407f-b9f6-3e7bbd878e33",
   "metadata": {},
   "source": [
    "# Initialize Online Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c48ba46-2e05-4723-a8af-e148f166ddef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random seed for reproducibility\n",
    "manual_seed(42)\n",
    "\n",
    "# Split features and labels\n",
    "X = online_df.drop(columns=['Label'])\n",
    "y = online_df['Label']\n",
    "X, y = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6cc61be-8984-4a0b-b243-c4830a559356",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define MLP for online learning\n",
    "class MyMLP(nn.Module):\n",
    "    def __init__(self, n_features):\n",
    "        super(MyMLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(n_features, 32)  # Match LSTM hidden size\n",
    "        self.fc2 = nn.Linear(32, 19)  # Match LSTM output\n",
    "        self.nonlin = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "    def forward(self, X, **kwargs):\n",
    "        X = self.nonlin(self.fc1(X))\n",
    "        X = self.softmax(self.fc2(X))\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5853376-c072-43ff-9aa8-da8dc9a335eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the online model pipeline\n",
    "model_pipeline = compose.Pipeline(preprocessing.StandardScaler(),\n",
    "    classification.Classifier(\n",
    "        module=MyMLP, \n",
    "        loss_fn='cross_entropy',\n",
    "        optimizer_fn='adam')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4057000-a89e-4ca1-995b-10eeb1d692f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Real-time metrics setup for multi-class evaluation\n",
    "metrics_dict = {\n",
    "    'Accuracy': metrics.Accuracy(),    \n",
    "    'Precision': metrics.MacroPrecision(),  # Macro-average for multi-class\n",
    "    'Recall': metrics.MacroRecall(),\n",
    "    'F1-score': metrics.MacroF1()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d62b2a04-4a97-4e50-beeb-6de3d7f9fc10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lists for tracking metrics over time for visualization\n",
    "accuracy_values, precision_values, recall_values, f1_values = [], [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a8c1b1c7-640f-4bcf-a34c-f69ca2efc9bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Buffer for recent prediction accuracy\n",
    "accuracy_buffer_offline = deque(maxlen=100)\n",
    "accuracy_buffer_online = deque(maxlen=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e8ca684-b52e-4eac-b6f3-efff34237c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set accuracy threshold for triggering online model updates\n",
    "accuracy_threshold = 0.99  # Define your acceptable threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8565b2c4-64de-4814-9225-26979537b375",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Real-time metrics visualization function\n",
    "def plot_metrics():\n",
    "    clear_output(wait=True)    \n",
    "    plt.figure(figsize=(8, 4))    \n",
    "    plt.plot(accuracy_values, label='Accuracy', color='blue', linestyle='--', alpha=0.7)    \n",
    "    plt.plot(precision_values, label='Precision', color='green', linestyle='--', alpha=0.7)        \n",
    "    plt.plot(recall_values, label='Recall', color='orange', linestyle='--', alpha=0.7)        \n",
    "    plt.plot(f1_values, label='F1-Score', color='red', linestyle='--', alpha=0.7)\n",
    "        \n",
    "    # Set title and labels\n",
    "    plt.title('Real-Time Metrics | RF–MLP | Class 19', fontsize=10)\n",
    "    plt.xlabel('Number of Samples')\n",
    "    plt.ylabel('Metric Value')\n",
    "    \n",
    "    # Add grid and legend\n",
    "    plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    plt.legend(loc='best')\n",
    "    \n",
    "    # Show plot   \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf627040-b695-4f14-abd8-66adf8411dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to update accuracy buffer\n",
    "def update_accuracy_buffer(buffer, y_true, y_pred):\n",
    "    buffer.append(int(y_true == y_pred))\n",
    "    return sum(buffer) / len(buffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f0f4931-2b80-4869-b47c-1abb07f727c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Streaming the dataset\n",
    "dataset = stream.iter_pandas(X, y)\n",
    "num_samples = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c3b702c-a412-44e9-9c76-8b3779c13186",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize tracking variables\n",
    "num_updates = 0\n",
    "agreement_count = 0\n",
    "confidence_scores = []\n",
    "total_time = 0\n",
    "degradation_instances = 0\n",
    "hybrid_predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "603e9a8d-e2b6-411e-952e-0f45b45b1740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Main loop for hybrid prediction\n",
    "for x, y in dataset:\n",
    "    start_time = time.time()  # Start time for runtime measurement\n",
    "    \n",
    "    num_samples += 1\n",
    "    x_scaled = scaler.transform(pd.DataFrame([x]))  # Scale each streamed instance\n",
    "    y_pred_offline = offline_model.predict(x_scaled)[0]\n",
    "    y_pred_online = model_pipeline.predict_one(x)\n",
    "\n",
    "    # Update accuracy buffers\n",
    "    offline_accuracy = update_accuracy_buffer(accuracy_buffer_offline, y, y_pred_offline)\n",
    "    online_accuracy = update_accuracy_buffer(accuracy_buffer_online, y, y_pred_online)\n",
    "\n",
    "    # Decision logic: Select prediction from model with higher recent accuracy\n",
    "    y_pred = y_pred_online if online_accuracy >= offline_accuracy else y_pred_offline\n",
    "\n",
    "    # Check agreement between offline and online models\n",
    "    if y_pred_offline == y_pred_online:\n",
    "        agreement_count += 1\n",
    "\n",
    "    # Append prediction to hybrid_predictions list\n",
    "    hybrid_predictions.append(y_pred)\n",
    "\n",
    "     # Calculate confidence score for online prediction, if available\n",
    "    y_pred_online_prob = model_pipeline.predict_proba_one(x)\n",
    "    confidence_score = max(y_pred_online_prob.values()) if y_pred_online_prob else None\n",
    "    if confidence_score is not None:\n",
    "        confidence_scores.append(confidence_score)\n",
    "\n",
    "    # Update real-time metrics\n",
    "    for name, metric in metrics_dict.items():\n",
    "        metric.update(y, y_pred)\n",
    "    \n",
    "    # Append latest metrics for visualization\n",
    "    accuracy_values.append(metrics_dict['Accuracy'].get())    \n",
    "    precision_values.append(metrics_dict['Precision'].get())\n",
    "    recall_values.append(metrics_dict['Recall'].get())\n",
    "    f1_values.append(metrics_dict['F1-score'].get())\n",
    "       \n",
    "    # Online model learning triggered by accuracy threshold\n",
    "    if online_accuracy < accuracy_threshold:\n",
    "        model_pipeline.learn_one(x, y)\n",
    "        num_updates += 1\n",
    "        degradation_instances += 1\n",
    "           \n",
    "    # Plot metrics every 50 samples\n",
    "    if num_samples % 50 == 0:\n",
    "        plot_metrics()\n",
    "\n",
    "    # Runtime calculation\n",
    "    end_time = time.time()\n",
    "    total_time += (end_time - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e0af668-5ae8-4e50-aae1-8f30cc8dc393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Performance Metrics of Hybrid Model:\n",
      "Accuracy: 0.9910\n",
      "Precision: 0.9122\n",
      "Recall: 0.8088\n",
      "F1-score: 0.8158\n"
     ]
    }
   ],
   "source": [
    "# Final results after streaming\n",
    "print(\"Final Performance Metrics of Hybrid Model:\")\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f\"{name}: {metric.get():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "deffe220-2722-4633-8b5f-4a25e4bf095d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Case Description:\n",
      "   Processed Samples Predicted Attack Type Model Source\n",
      "0             161212             DDoS-ICMP       Online\n",
      "\n",
      "Hybrid Model Prediction Distribution:\n",
      "           Attack Type  Count\n",
      "0            DDoS-ICMP  34916\n",
      "1             DDoS-SYN  17175\n",
      "2             DDoS-UDP  36413\n",
      "3             DoS-ICMP   9753\n",
      "4               Benign   3984\n",
      "5   DDoS-Connect_Flood   4116\n",
      "6             DDoS-TCP  18360\n",
      "7              DoS-TCP   8098\n",
      "8              DoS-SYN   9890\n",
      "9              DoS-UDP  13741\n",
      "10   DoS-Publish_Flood   1636\n",
      "11             OS_Scan    214\n",
      "12           Port_Scan   2197\n",
      "13        ARP_Spoofing    234\n",
      "14      Malformed_Data     99\n",
      "15   DoS-Connect_Flood    279\n",
      "16  DDoS-Publish_Flood     77\n",
      "17          Ping_Sweep     16\n",
      "18             VulScan     14\n"
     ]
    }
   ],
   "source": [
    "# Reverse the dictionary to map index to attack type\n",
    "index_to_attack = {v: k for k, v in attacks.items()}\n",
    "\n",
    "# Determine the predicted attack type\n",
    "predicted_attack_type = index_to_attack.get(y_pred, \"Unknown\")\n",
    "online_prediction_type = index_to_attack.get(y_pred_online, \"Unknown\")\n",
    "\n",
    "# Generate Hybrid Model Prediction Distribution\n",
    "hybrid_pred_distribution = Counter(hybrid_predictions)\n",
    "hybrid_distribution_table = pd.DataFrame([\n",
    "    {\"Attack Type\": index_to_attack.get(k, \"Unknown\"), \"Count\": v}\n",
    "    for k, v in hybrid_pred_distribution.items()\n",
    "])\n",
    "\n",
    "# Print Results as Table and Case Description\n",
    "case_description = {\n",
    "    \"Processed Samples\": num_samples,\n",
    "    \"Predicted Attack Type\": predicted_attack_type,\n",
    "    \"Model Source\": \"Online\" if y_pred == y_pred_online else \"Offline\",\n",
    "}\n",
    "\n",
    "# Display Results\n",
    "print(\"Case Description:\")\n",
    "print(pd.DataFrame([case_description]))\n",
    "\n",
    "print(\"\\nHybrid Model Prediction Distribution:\")\n",
    "print(hybrid_distribution_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f5f19bb3-dda9-4f85-b29e-d948e9fdd1ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Offline Model Accuracy: 0.9800\n",
      "Average Online Model Accuracy: 0.8300\n",
      "Prediction Agreement Rate: 0.7579\n",
      "Average Online Model Confidence Score: 0.1305\n",
      "Recent Accuracy (last 100 samples): 0.8300\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average Offline Model Accuracy: {offline_accuracy:.4f}\")\n",
    "print(f\"Average Online Model Accuracy: {online_accuracy:.4f}\")\n",
    "print(f\"Prediction Agreement Rate: {agreement_count / num_samples:.4f}\")\n",
    "if confidence_scores:\n",
    "    avg_confidence = sum(confidence_scores) / len(confidence_scores)\n",
    "    print(f\"Average Online Model Confidence Score: {avg_confidence:.4f}\")\n",
    "recent_accuracy = sum(accuracy_buffer_online) / len(accuracy_buffer_online)\n",
    "print(f\"Recent Accuracy (last 100 samples): {recent_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d7a57a8d-5049-408c-a185-5ee559da2190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Online Model Updates: 161212\n",
      "Performance Degradation Instances: 161212\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total Online Model Updates: {num_updates}\")\n",
    "print(f\"Performance Degradation Instances: {degradation_instances}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "20eeb8de-54f4-4ee9-b056-74cf30dd1987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Runtime per Sample: 0.0124 seconds\n",
      "Throughput (samples per second): 80.90\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average Runtime per Sample: {total_time / num_samples:.4f} seconds\")\n",
    "throughput = num_samples / total_time\n",
    "print(f\"Throughput (samples per second): {throughput:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "17972ba6-3270-4e28-a66d-2f84bd669d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Threshold for Online Model Updates: 0.99\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy Threshold for Online Model Updates: {accuracy_threshold}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5dff08ed-7244-4e8f-85e5-3e0e4e8baab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Logic: Hybrid Prediction\n",
      " - Prediction selected from the model with higher recent accuracy (last 100 samples).\n",
      " - If offline and online predictions are equal, preference is given to the online model.\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Logic: Hybrid Prediction\")\n",
    "print(\" - Prediction selected from the model with higher recent accuracy (last 100 samples).\")\n",
    "print(\" - If offline and online predictions are equal, preference is given to the online model.\")"
   ]
  }
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