RF-CNN_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 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": "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-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",
       "}\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",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\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",
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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",
       "  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": "d8cea04c-c50d-4279-8ddc-11e6fa06cd0b",
   "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), 19)  # 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": 15,
   "id": "bb21c0ef-83e1-4860-911f-7f52c1ddc6e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the online model pipeline\n",
    "model_pipeline = preprocessing.StandardScaler() | classification.Classifier(\n",
    "    module=MyCNN,    \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–CNN | 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: 0.9909\n",
      "Precision: 0.9105\n",
      "Recall: 0.8108\n",
      "F1-score: 0.8168\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  34912\n",
      "1             DDoS-SYN  17182\n",
      "2             DDoS-UDP  36409\n",
      "3             DoS-ICMP   9754\n",
      "4               Benign   3965\n",
      "5   DDoS-Connect_Flood   4115\n",
      "6             DDoS-TCP  18367\n",
      "7              DoS-TCP   8097\n",
      "8              DoS-SYN   9889\n",
      "9              DoS-UDP  13734\n",
      "10   DoS-Publish_Flood   1635\n",
      "11             OS_Scan    211\n",
      "12           Port_Scan   2187\n",
      "13        ARP_Spoofing    265\n",
      "14      Malformed_Data    101\n",
      "15   DoS-Connect_Flood    280\n",
      "16  DDoS-Publish_Flood     78\n",
      "17          Ping_Sweep     16\n",
      "18             VulScan     15\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: 0.7300\n",
      "Prediction Agreement Rate: 0.6979\n",
      "Average Online Model Confidence Score: 0.1308\n",
      "Recent Accuracy (last 100 samples): 0.7300\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: 0.0147 seconds\n",
      "Throughput (samples per second): 68.20\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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