AutoML-based_IDS_Online_Learning_Dataset_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study: Automated Machine Learning (AutoML) for Autonomous Intrusion Detection System Development \n",
    "This is the code for the paper entitled \"**[Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://ieeexplore.ieee.org/document/10472316)**\" published in *IEEE Transactions on Network and Service Management* (IF:5.3).<br>\n",
    "Authors: Li Yang (liyanghart@gmail.com), Mirna El Rajab, Abdallah Shami, and Sami Muhaidat<br>\n",
    "\n",
    "L. Yang, M. E. Rajab, A. Shami, and S. Muhaidat, \"Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis,\" IEEE Transactions on Network and Service Management, pp. 1-28, 2024, doi: https://doi.org/10.1109/TNSM.2024.3376631."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Part 2: Automated Online Learning (Dynamic Data Stream Analytics)\n",
    "Online learning: Online learning techniques are able to train models using continuously incoming online data streams in dynamic environments. By learning a single data sample at a time, online learning models can reduce memory requirements for data storage and learn new data patterns. Additionally, online learning models can often achieve real-time processing and address concept drift issues. Thus, when applied to dynamic data streams or when inadequate data is available, online learning is often more effective than batch learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset 2: 5G-NIDD\n",
    "A subset of the network traffic data randomly sampled from the [5G-NIDD dataset](https://ieee-dataport.org/documents/5g-nidd-comprehensive-network-intrusion-detection-dataset-generated-over-5g-wireless).  \n",
    "\n",
    "The 5G-NIDD dataset, created in December 2022, is a fully labeled resource constructed on a functional 5G test network for researchers and practitioners evaluating AI/ML solutions in the context of 5G/6G security [87]. 5G-NIDD encompasses data extracted from a 5G testbed connected to the 5G Test Network (5GTN) at the University of Oulu, Finland. The dataset is derived from two base stations, each featuring an attacker node and multiple benign 5G users. The attacker nodes target a server deployed within the 5GTN MEC environment. The attack scenarios captured in the dataset primarily include DoS attacks and port scans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score, precision_score, recall_score, f1_score\n",
    "import lightgbm as lgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.feature_selection import RFE\n",
    "from scipy.stats import shapiro\n",
    "from imblearn.over_sampling import SMOTE, ADASYN\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the sampled 5G-NIDD dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data/5gnidd_0.01_pre-processed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
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       "            Seq           Dur       RunTime          Mean           Sum  \\\n",
       "0      0.000496  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "1      0.002442  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "2      0.003295  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "3      0.003317  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "4      0.003375  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "...         ...           ...           ...           ...           ...   \n",
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       "12158  0.000015  4.088011e-02  4.088011e-02  4.088011e-02  4.088011e-02   \n",
       "\n",
       "                Min           Max  Proto      sTos      dTos  ...    SrcWin  \\\n",
       "0      0.000000e+00  0.000000e+00   0.75  0.000000  0.000000  ...  0.000002   \n",
       "1      0.000000e+00  0.000000e+00   0.75  0.000000  0.000000  ...  0.000000   \n",
       "2      0.000000e+00  0.000000e+00   0.75  0.000000  0.000000  ...  0.000000   \n",
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       "4      0.000000e+00  0.000000e+00   0.75  0.000000  0.000000  ...  0.000000   \n",
       "...             ...           ...    ...       ...       ...  ...       ...   \n",
       "12154  0.000000e+00  0.000000e+00   1.00  0.000000  0.000000  ...  0.000000   \n",
       "12155  2.000005e-07  2.000005e-07   0.75  0.000000  0.000000  ...  0.000008   \n",
       "12156  0.000000e+00  0.000000e+00   1.00  0.000000  0.000000  ...  0.000000   \n",
       "12157  4.079091e-02  4.079091e-02   0.50  0.830357  0.215054  ...  0.000000   \n",
       "12158  4.088011e-02  4.088011e-02   0.50  0.830357  0.215054  ...  0.000000   \n",
       "\n",
       "        DstWin  sVid  dVid  SrcTCPBase  DstTCPBase  TcpRtt  SynAck  AckDat  \\\n",
       "0      0.00000   0.0   0.0    0.388654    0.000000     0.0     0.0     0.0   \n",
       "1      0.00000   0.0   0.0    0.388654    0.000000     0.0     0.0     0.0   \n",
       "2      0.00000   0.0   0.0    0.388654    0.000000     0.0     0.0     0.0   \n",
       "3      0.00000   0.0   0.0    0.388654    0.000000     0.0     0.0     0.0   \n",
       "4      0.00000   0.0   0.0    0.388669    0.000000     0.0     0.0     0.0   \n",
       "...        ...   ...   ...         ...         ...     ...     ...     ...   \n",
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       "12158  0.00000   0.0   1.0    0.000000    0.000000     0.0     0.0     0.0   \n",
       "\n",
       "       Label  \n",
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       "4          1  \n",
       "...      ...  \n",
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       "\n",
       "[12159 rows x 49 columns]"
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     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Automated Data Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Transformation/Encoding\n",
    "Automatically identify and transform string/text features into numerical features to make the data more readable by ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the automated data encoding function\n",
    "def Auto_Encoding(df):\n",
    "    cat_features=[x for x in df.columns if df[x].dtype==\"object\"] ## Find string/text features\n",
    "    le=LabelEncoder()\n",
    "    for col in cat_features:\n",
    "        if col in df.columns:\n",
    "            i = df.columns.get_loc(col)\n",
    "            # Transform to numerical features\n",
    "            df.iloc[:,i] = df.apply(lambda i:le.fit_transform(i.astype(str)), axis=0, result_type='expand')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=Auto_Encoding(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated normalization\n",
    "Normalize the range of features to a similar scale to improve data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def Auto_Normalization(df):\n",
    "    stat, p = shapiro(df)\n",
    "    print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "    # interpret\n",
    "    alpha = 0.05\n",
    "    numeric_features = df.drop(['Label'],axis = 1).dtypes[df.dtypes != 'object'].index\n",
    "    \n",
    "    # check if the data distribution follows a Gaussian/normal distribution\n",
    "    # If so, select the Z-score normalization method; otherwise, select the min-max normalization\n",
    "    # Details are in the paper\n",
    "    if p > alpha:\n",
    "        print('Sample looks Gaussian (fail to reject H0)')\n",
    "        df[numeric_features] = df[numeric_features].apply(\n",
    "            lambda x: (x - x.mean()) / (x.std()))\n",
    "        print('Z-score normalization is automatically chosen and used')\n",
    "    else:\n",
    "        print('Sample does not look Gaussian (reject H0)')\n",
    "        df[numeric_features] = df[numeric_features].apply(\n",
    "            lambda x: (x - x.min()) / (x.max()-x.min()))\n",
    "        print('Min-max normalization is automatically chosen and used')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.563, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n",
      "Min-max normalization is automatically chosen and used\n"
     ]
    }
   ],
   "source": [
    "df=Auto_Normalization(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Imputation\n",
    "Detect and impute missing values to improve data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the automated data imputation function\n",
    "def Auto_Imputation(df):\n",
    "    # Replace infinities with NaN to unify the treatment of missing data\n",
    "    df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "    \n",
    "    # Identify numeric columns\n",
    "    numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    \n",
    "    # Apply imputation only if necessary\n",
    "    if df[numeric_cols].isnull().values.any():\n",
    "        for col in numeric_cols:\n",
    "            # Check if the entire column is NaN\n",
    "            if df[col].isna().all():\n",
    "                # Optional: fill such columns with a placeholder value or drop them\n",
    "                # Here, we choose to fill with 0 as an example\n",
    "                df[col].fillna(0, inplace=True)\n",
    "            else:\n",
    "                # Apply median imputation\n",
    "                df[col].fillna(df[col].median(), inplace=True)\n",
    "    \n",
    "    # Final check for NaN or infinite values in numeric columns\n",
    "    if df[numeric_cols].isnull().any().any() or np.isinf(df[numeric_cols].values).any():\n",
    "        raise ValueError(\"Numeric data still contains NaN, infinity or a value too large after imputation.\")\n",
    "    \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=Auto_Imputation(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Automated Feature Engineering\n",
    "Feature selection method 1: **Recursive Feature Elimination (RFE)**, used to remove irrelevant features to improve model efficiency  \n",
    "Feature selection method 2: **Pearson Correlation**, used to remove redundant features to improve model efficiency and accuracy  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove irrelevant features and select important features\n",
    "def Feature_Importance_RFE(data, n_features_to_select=20):\n",
    "    features = data.drop(['Label'], axis=1).values  # \"Label\" should be changed to the target class variable name if different\n",
    "    labels = data['Label'].values\n",
    "\n",
    "    # Extract feature names\n",
    "    feature_names = list(data.drop(['Label'], axis=1).columns)\n",
    "\n",
    "    # Create a base estimator\n",
    "    model = lgb.LGBMRegressor(verbose = -1)\n",
    "\n",
    "    # Create the RFE object and rank each feature\n",
    "    rfe = RFE(estimator=model, n_features_to_select=n_features_to_select, step=1)\n",
    "    rfe.fit(features, labels)\n",
    "\n",
    "    # Get the feature ranking\n",
    "    feature_ranking = rfe.ranking_\n",
    "\n",
    "    # Create a DataFrame for feature importances\n",
    "    feature_importances = pd.DataFrame({'feature': feature_names, 'ranking': feature_ranking})\n",
    "\n",
    "    # Sort features according to their ranking\n",
    "    feature_importances = feature_importances.sort_values('ranking', ascending=True).reset_index(drop=True)\n",
    "\n",
    "    # Get the features to drop\n",
    "    to_drop = list(feature_importances[feature_importances['ranking'] > 1]['feature'])\n",
    "\n",
    "    return to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove redundant features\n",
    "def Feature_Redundancy_Pearson(data):\n",
    "    correlation_threshold=0.90 # Only remove features with the redundancy>90%. It can be changed\n",
    "    features = data.drop(['Label'],axis=1)\n",
    "    corr_matrix = features.corr()\n",
    "\n",
    "    # Extract the upper triangle of the correlation matrix\n",
    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))\n",
    "\n",
    "    # Select the features with correlations above the threshold\n",
    "    # Need to use the absolute value\n",
    "    to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]\n",
    "\n",
    "    # Dataframe to hold correlated pairs\n",
    "    record_collinear = pd.DataFrame(columns = ['drop_feature', 'corr_feature', 'corr_value'])\n",
    "\n",
    "    # Iterate through the columns to drop\n",
    "    for column in to_drop:\n",
    "\n",
    "        # Find the correlated features\n",
    "        corr_features = list(upper.index[upper[column].abs() > correlation_threshold])\n",
    "\n",
    "        # Find the correlated values\n",
    "        corr_values = list(upper[column][upper[column].abs() > correlation_threshold])\n",
    "        drop_features = [column for _ in range(len(corr_features))]    \n",
    "\n",
    "        # Record the information (need a temp df for now)\n",
    "        temp_df = pd.DataFrame.from_dict({'drop_feature': drop_features,\n",
    "                                         'corr_feature': corr_features,\n",
    "                                         'corr_value': corr_values})\n",
    "        record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
    "#     print(record_collinear)\n",
    "    return to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Auto_Feature_Engineering(df):\n",
    "    drop1 = Feature_Importance_RFE(df)\n",
    "    dfh1 = df.drop(columns = drop1)\n",
    "    \n",
    "    drop2 = Feature_Redundancy_Pearson(dfh1)\n",
    "    dfh2 = dfh1.drop(columns = drop2)\n",
    "    \n",
    "    return dfh2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seq</th>\n",
       "      <th>Dur</th>\n",
       "      <th>Proto</th>\n",
       "      <th>sTtl</th>\n",
       "      <th>dTtl</th>\n",
       "      <th>sHops</th>\n",
       "      <th>TotBytes</th>\n",
       "      <th>SrcBytes</th>\n",
       "      <th>Offset</th>\n",
       "      <th>sMeanPktSz</th>\n",
       "      <th>...</th>\n",
       "      <th>Load</th>\n",
       "      <th>pLoss</th>\n",
       "      <th>Rate</th>\n",
       "      <th>SrcWin</th>\n",
       "      <th>DstWin</th>\n",
       "      <th>SrcTCPBase</th>\n",
       "      <th>TcpRtt</th>\n",
       "      <th>SynAck</th>\n",
       "      <th>AckDat</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000496</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.388654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002442</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.145098</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.388654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003295</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.149020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.001133</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.388654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.388654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003375</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>0.042208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.388669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12154</th>\n",
       "      <td>0.003688</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.976471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.003075</td>\n",
       "      <td>0.053852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12155</th>\n",
       "      <td>0.004104</td>\n",
       "      <td>2.000005e-07</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.203922</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.003462</td>\n",
       "      <td>0.039298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.557175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.292755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12156</th>\n",
       "      <td>0.005234</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.976471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.004298</td>\n",
       "      <td>0.053852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12157</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>4.079091e-02</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.004546</td>\n",
       "      <td>0.069862</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12158</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>4.088011e-02</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.069862</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12159 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Seq           Dur  Proto      sTtl      dTtl     sHops  TotBytes  \\\n",
       "0      0.000496  0.000000e+00   0.75  0.211765  0.000000  0.357143  0.000007   \n",
       "1      0.002442  0.000000e+00   0.75  0.145098  0.000000  0.964286  0.000007   \n",
       "2      0.003295  0.000000e+00   0.75  0.149020  0.000000  0.928571  0.000007   \n",
       "3      0.003317  0.000000e+00   0.75  0.211765  0.000000  0.357143  0.000007   \n",
       "4      0.003375  0.000000e+00   0.75  0.211765  0.000000  0.357143  0.000007   \n",
       "...         ...           ...    ...       ...       ...       ...       ...   \n",
       "12154  0.003688  0.000000e+00   1.00  0.976471  0.000000  0.250000  0.000014   \n",
       "12155  0.004104  2.000005e-07   0.75  0.203922  0.250980  0.428571  0.000158   \n",
       "12156  0.005234  0.000000e+00   1.00  0.976471  0.000000  0.250000  0.000014   \n",
       "12157  0.000015  4.079091e-02   0.50  1.000000  0.980392  0.035714  0.000145   \n",
       "12158  0.000015  4.088011e-02   0.50  1.000000  0.980392  0.035714  0.000145   \n",
       "\n",
       "       SrcBytes    Offset  sMeanPktSz  ...      Load  pLoss      Rate  \\\n",
       "0      0.000092  0.000201    0.042208  ...  0.000000    0.0  0.000000   \n",
       "1      0.000092  0.000850    0.042208  ...  0.000000    0.0  0.000000   \n",
       "2      0.000092  0.001133    0.042208  ...  0.000000    0.0  0.000000   \n",
       "3      0.000092  0.001140    0.042208  ...  0.000000    0.0  0.000000   \n",
       "4      0.000092  0.001160    0.042208  ...  0.000000    0.0  0.000000   \n",
       "...         ...       ...         ...  ...       ...    ...       ...   \n",
       "12154  0.000117  0.003075    0.053852  ...  0.000000    0.0  0.000000   \n",
       "12155  0.000257  0.003462    0.039298  ...  0.557175    0.0  1.000000   \n",
       "12156  0.000117  0.004298    0.053852  ...  0.000000    0.0  0.000000   \n",
       "12157  0.000304  0.004546    0.069862  ...  0.000002    0.0  0.000003   \n",
       "12158  0.000304  0.004757    0.069862  ...  0.000002    0.0  0.000003   \n",
       "\n",
       "         SrcWin   DstWin  SrcTCPBase  TcpRtt  SynAck  AckDat  Label  \n",
       "0      0.000002  0.00000    0.388654     0.0     0.0     0.0      1  \n",
       "1      0.000000  0.00000    0.388654     0.0     0.0     0.0      1  \n",
       "2      0.000000  0.00000    0.388654     0.0     0.0     0.0      1  \n",
       "3      0.000000  0.00000    0.388654     0.0     0.0     0.0      1  \n",
       "4      0.000000  0.00000    0.388669     0.0     0.0     0.0      1  \n",
       "...         ...      ...         ...     ...     ...     ...    ...  \n",
       "12154  0.000000  0.00000    0.000000     0.0     0.0     0.0      0  \n",
       "12155  0.000008  0.00025    0.292755     0.0     0.0     0.0      0  \n",
       "12156  0.000000  0.00000    0.000000     0.0     0.0     0.0      0  \n",
       "12157  0.000000  0.00000    0.000000     0.0     0.0     0.0      0  \n",
       "12158  0.000000  0.00000    0.000000     0.0     0.0     0.0      0  \n",
       "\n",
       "[12159 rows x 21 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfh2 = Auto_Feature_Engineering(df)\n",
    "dfh2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Train-test split\n",
    "Split the dataset into the training and the test set   \n",
    "\n",
    "Here we used the 1%/99% split, because for online learning, the initial available data would only be a small percentage, and the incoming data streams will be continously learned by the data analytics models. It can be changed based on specific tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfh2.drop(['Label'],axis=1)\n",
    "y = dfh2['Label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size = 0.01, test_size = 0.99, shuffle=False,random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated data balancing\n",
    "Generate minority class samples to solve class-imbalance and improve data quality.  \n",
    "Synthetic Minority Over-sampling Technique (SMOTE) method is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    109\n",
       "0     12\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For binary data (can be modified for multi-class data with the same logic)\n",
    "def Auto_Balancing(X_train, y_train):\n",
    "    number0 = pd.Series(y_train).value_counts().iloc[0]\n",
    "    number1 = pd.Series(y_train).value_counts().iloc[1]\n",
    "    \n",
    "    if number0 > number1:\n",
    "        nlarge = number0\n",
    "    else:\n",
    "        nlarge = number1\n",
    "    \n",
    "    # evaluate whether the incoming dataset is imbalanced (the abnormal/normal ratio is smaller than a threshold (e.g., 50%)) \n",
    "    if (number1/number0 > 1.5) or (number0/number1 > 1.5):\n",
    "        balanced=ADASYN(n_jobs=-1,sampling_strategy={0:nlarge, 1:nlarge})\n",
    "\n",
    "        X_train, y_train = balanced.fit_resample(X_train, y_train)\n",
    "        \n",
    "    return X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = Auto_Balancing(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    109\n",
       "0    108\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Model Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four state-of-the-art online learners: \n",
    "* Hoeffding Tree (HT)\n",
    "* Leveraging Bagging (LB)\n",
    "* Adaptive Random Forest (ARF) \n",
    "* Streaming Random Patches (SRP)\n",
    "\n",
    "\n",
    "An offline learner for comparison:\n",
    "* LightGBM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static Model learning: LightGBM (for Comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.17      0.22      4731\n",
      "           1       0.58      0.73      0.64      7307\n",
      "\n",
      "    accuracy                           0.51     12038\n",
      "   macro avg       0.43      0.45      0.43     12038\n",
      "weighted avg       0.46      0.51      0.48     12038\n",
      "\n",
      "Accuracy: 50.946999999999996%\n",
      "Precision: 57.587999999999994%\n",
      "Recall: 72.807%\n",
      "F1-score: 64.30900000000001%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 191 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# LightGBM algorithm\n",
    "import lightgbm as lgb\n",
    "classifier = lgb.LGBMClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "print(\"Accuracy: \"+str(round(accuracy_score(y_test, y_pred),5)*100)+\"%\")\n",
    "print(\"Precision: \"+str(round(precision_score(y_test, y_pred),5)*100)+\"%\")\n",
    "print(\"Recall: \"+str(round(recall_score(y_test, y_pred),5)*100)+\"%\")\n",
    "print(\"F1-score: \"+str(round(f1_score(y_test, y_pred),5)*100)+\"%\")\n",
    "\n",
    "# Plot the confusion matrix\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "f,ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidth=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_true\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of the static LightGBM model is very low (83.958%), due to the occurance of concept drift (data distribution changes). Therefore, online learning methods that can adapt to concept drift issues should be developed for dynamic data stream analytics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Online Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the online learning metrics and algorithms from the River library\n",
    "import river\n",
    "from river import metrics\n",
    "from river import stream\n",
    "from river import tree,neighbors,naive_bayes,ensemble,linear_model\n",
    "from river.drift import DDM, EDDM, ADWIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a generic adaptive learning function\n",
    "# The argument \"model\" means an online adaptive learning algorithm\n",
    "def adaptive_learning(model, X_train, y_train, X_test, y_test):\n",
    "    metric = metrics.Accuracy() # Use accuracy as the metric\n",
    "    i = 0 # count the number of evaluated data points\n",
    "    t = [] # record the number of evaluated data points\n",
    "    m = [] # record the real-time accuracy\n",
    "    yt = [] # record all the true labels of the test set\n",
    "    yp = [] # record all the predicted labels of the test set\n",
    "\n",
    "    # Learn the training set\n",
    "    for xi1, yi1 in stream.iter_pandas(X_train, y_train):\n",
    "        model.learn_one(xi1,yi1) \n",
    "\n",
    "    # Predict the test set\n",
    "    for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "        y_pred= model.predict_one(xi)  # Predict the test sample\n",
    "        model.learn_one(xi,yi) # Learn the test sample\n",
    "        metric = metric.update(yi, y_pred) # Update the real-time accuracy\n",
    "        t.append(i)\n",
    "        m.append(metric.get()*100)\n",
    "        yt.append(yi)\n",
    "        yp.append(y_pred)\n",
    "        i = i+1\n",
    "    print(\"Accuracy: \"+str(round(accuracy_score(yt,yp),5)*100)+\"%\")\n",
    "    print(\"Precision: \"+str(round(precision_score(yt,yp),5)*100)+\"%\")\n",
    "    print(\"Recall: \"+str(round(recall_score(yt,yp),5)*100)+\"%\")\n",
    "    print(\"F1-score: \"+str(round(f1_score(yt,yp),5)*100)+\"%\")\n",
    "    return t, m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a figure function that shows the real-time accuracy changes\n",
    "def acc_fig(t, m, name):\n",
    "    plt.rcParams.update({'font.size': 15})\n",
    "    plt.figure(1,figsize=(10,6)) \n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.clf() \n",
    "    plt.plot(t,m,'-b',label='Avg Accuracy: %.2f%%'%(m[-1]))\n",
    "\n",
    "    plt.legend(loc='best')\n",
    "    plt.title(name+' on 5G-NIDD dataset', fontsize=15)\n",
    "    plt.xlabel('Number of samples')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "\n",
    "    plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.877%\n",
      "Precision: 98.625%\n",
      "Recall: 96.195%\n",
      "F1-score: 97.395%\n",
      "Wall time: 2.89 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Hoeffding Tree (HT) model \n",
    "name1 = \"HT model\"\n",
    "model1 = tree.HoeffdingTreeClassifier() # Define the model\n",
    "t, m1 = adaptive_learning(model1, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m1, name1) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.762%\n",
      "Precision: 98.39099999999999%\n",
      "Recall: 99.589%\n",
      "F1-score: 98.98700000000001%\n",
      "Wall time: 3.66 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the K-Nearest Neighbors (KNN) classifier with ADWIN change detector\n",
    "name2 = \"KNN-ADWIN model\"\n",
    "model2 = neighbors.KNNADWINClassifier(window_size=100) # Define the model\n",
    "t, m2 = adaptive_learning(model2, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m2, name2) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.97%\n",
      "Precision: 99.259%\n",
      "Recall: 99.042%\n",
      "F1-score: 99.151%\n",
      "Wall time: 6.36 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Adaptive Random Forest (ARF) model\n",
    "name3 = \"ARF model\"\n",
    "model3 = ensemble.AdaptiveRandomForestClassifier(n_models = 3) # Define the model\n",
    "t, m3 = adaptive_learning(model3, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m3, name3) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.327%\n",
      "Precision: 99.344%\n",
      "Recall: 99.548%\n",
      "F1-score: 99.446%\n",
      "Wall time: 9.8 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Adaptive Random Forest (ARF) model\n",
    "name32 = \"ARF model\"\n",
    "model32 = ensemble.AdaptiveRandomForestClassifier(n_models = 4) # Define the model\n",
    "t, m32 = adaptive_learning(model32, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m32, name32) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.419%\n",
      "Precision: 99.453%\n",
      "Recall: 99.589%\n",
      "F1-score: 99.521%\n",
      "Wall time: 26.9 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Streaming Random Patches (SRP) model with ADWIN drift detector\n",
    "name4 = \"SRP model\"\n",
    "model4 = ensemble.SRPClassifier(n_models = 3) # Define the model\n",
    "t, m4 = adaptive_learning(model4, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m4, name4) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get optimized SRP models\n",
    "The optimization process can be found at: https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics/blob/main/AutoML_Online_Learning_Dataset_2.ipynb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.52600000000001%\n",
      "Precision: 99.562%\n",
      "Recall: 99.658%\n",
      "F1-score: 99.61%\n",
      "Wall time: 1min 10s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Use the Streaming Random Patches (SRP) model with ADWIN drift detector\n",
    "name5 = \"Optimized SRP model\"\n",
    "model5 = ensemble.SRPClassifier(n_models = 8) # Define the model\n",
    "t, m5 = adaptive_learning(model5, X_train, y_train, X_test, y_test) # Learn the model on the dataset\n",
    "acc_fig(t, m5, name5) # Draw the figure of how the real-time accuracy changes with the number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a comprehensive figure to compare the performance of all models\n",
    "plt.rcParams.update({'font.size': 30})\n",
    "plt.figure(1,figsize=(24,15)) \n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.clf() \n",
    "\n",
    "# Plot the accuracy change of each learner\n",
    "plt.plot(t,m1,'-b',label=name1+', Avg Accuracy: %.3f%%'%(m1[-1]))\n",
    "plt.plot(t,m2,'-g',label=name2+', Avg Accuracy: %.3f%%'%(m2[-1]))\n",
    "plt.plot(t,m3,'orange',label=name3+', Avg Accuracy: %.3f%%'%(m3[-1]))\n",
    "plt.plot(t,m4,'black',label=name4+', Avg Accuracy: %.3f%%'%(m4[-1]))\n",
    "plt.plot(t,m5,'red',label=name5+', Avg Accuracy: %.3f%%'%(m5[-1]))\n",
    "\n",
    "# Draw the drift points/time\n",
    "dr = [0,2190,5080,7380,10500]\n",
    "for i in range(len(dr)):\n",
    "    if i!=0:\n",
    "        plt.text(dr[i]-500, 100.2, 'Drift '+str(i), c = \"red\", fontsize = 25)\n",
    "        plt.vlines(dr[i], 0, 100, colors = \"red\", linewidth=4, linestyles = \"dashed\")\n",
    "        \n",
    "plt.legend(loc='lower right')\n",
    "plt.ylim(85, 100.8)\n",
    "plt.title('Online Learning Methods Comparison on 5G-NIDD', fontsize=40)\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "\n",
    "plt.draw()"
   ]
  }
 ],
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