RF-CNN_CICIoMT2024_2.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 torch.nn import functional as F\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": "23956691-06f7-4f97-92af-51eae0bc9981",
   "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": "b60e435a-c00f-4a76-b892-b9ccaabb758f",
   "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": "2248b404-ecb6-45c8-bd99-0348f7117521",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace all values in 'Label' column with 'Attack', except 'Benign'\n",
    "offline_df.loc[offline_df['Label'] != 'Benign', 'Label'] = 'Attack'\n",
    "online_df.loc[online_df['Label'] != 'Benign', 'Label'] = 'Attack'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea8e57c1-357f-41f5-970e-73d8da5153fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\n",
      "Attack    69643\n",
      "Benign     1914\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(offline_df['Label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e24d32ea-fb6d-46bf-acb7-68c16c1e316d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\n",
      "Attack    157423\n",
      "Benign      3789\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(online_df['Label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2af9f88-66b5-4a87-9237-dac462e10acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dictionary of Types\n",
    "attacks = {'Attack':0, 'Benign':1}\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": 12,
   "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": 13,
   "id": "e4a90418-8620-4ebc-8d73-cb81e3a15c59",
   "metadata": {},
   "outputs": [
    {
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       "  /* 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-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --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",
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       "\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",
       "#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",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
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       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  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",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
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       "\n",
       "#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",
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       "  padding-left: 1em;\n",
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       "\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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       "\n",
       "#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": 13,
     "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": 14,
   "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": 15,
   "id": "00e8cb84-824c-468a-8a72-05331685f6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define CNN for online learning\n",
    "class MyCNN(nn.Module):\n",
    "    def __init__(self, n_features):\n",
    "        super(MyCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=1)\n",
    "        self.pool = nn.MaxPool1d(kernel_size=1, stride=1)\n",
    "        self.fc = nn.Linear(32 * (n_features // 1), 2)  # Match LSTM final output\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "    def forward(self, X, **kwargs):\n",
    "        X = X.unsqueeze(1)  # Add a channel dimension for Conv1D\n",
    "        X = self.pool(F.relu(self.conv1(X)))\n",
    "        X = X.view(X.size(0), -1)  # Flatten tensor\n",
    "        X = self.softmax(self.fc(X))\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2220fab5-640a-40be-91b3-ebf458471f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the online model pipeline\n",
    "model_pipeline = preprocessing.StandardScaler() | classification.Classifier(\n",
    "    module=MyCNN,    \n",
    "    loss_fn='binary_cross_entropy',\n",
    "    optimizer_fn='adam'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4057000-a89e-4ca1-995b-10eeb1d692f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Real-time metrics setup\n",
    "metrics_dict = {\n",
    "    'Accuracy': metrics.Accuracy(),    \n",
    "    'Precision': metrics.Precision(),\n",
    "    'Recall': metrics.Recall(),\n",
    "    'F1-score': metrics.F1()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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–CNN | Class 2', 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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "id": "6e0af668-5ae8-4e50-aae1-8f30cc8dc393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Performance Metrics of Hybrid Model:\n",
      "Accuracy: 0.9987\n",
      "Precision: 0.9673\n",
      "Recall: 0.9773\n",
      "F1-score: 0.9723\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": 27,
   "id": "3a161a19-87b4-431d-8f42-5c65f562ffd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Case Description:\n",
      "   Processed Samples Predicted Attack Type Model Source\n",
      "0             161212                Attack       Online\n",
      "\n",
      "Hybrid Model Prediction Distribution:\n",
      "  Attack Type   Count\n",
      "0      Attack  157384\n",
      "1      Benign    3828\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": 28,
   "id": "f5f19bb3-dda9-4f85-b29e-d948e9fdd1ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Offline Model Accuracy: 1.0000\n",
      "Average Online Model Accuracy: 0.9900\n",
      "Prediction Agreement Rate: 0.9957\n",
      "Average Online Model Confidence Score: 0.7295\n",
      "Recent Accuracy (last 100 samples): 0.9900\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": 29,
   "id": "d7a57a8d-5049-408c-a185-5ee559da2190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Online Model Updates: 11114\n",
      "Performance Degradation Instances: 11114\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total Online Model Updates: {num_updates}\")\n",
    "print(f\"Performance Degradation Instances: {degradation_instances}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "20eeb8de-54f4-4ee9-b056-74cf30dd1987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Runtime per Sample: 0.0101 seconds\n",
      "Throughput (samples per second): 99.16\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": 31,
   "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": 32,
   "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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