AutoML-based_IDS_Online_Learning_Dataset_1.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 1: CICIDS2017\n",
    "A subset of the network traffic data randomly sampled from the [CICIDS2017 dataset](https://www.unb.ca/cic/datasets/ids-2017.html).  \n",
    "\n",
    "The Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) dataset has the most updated network threats. The CICIDS2017 dataset is close to real-world network data since it has a large amount of network traffic data, a variety of network features, various types of attacks, and highly imbalanced classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the sampled CICIDS2017 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data/cic_0.01km.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       Flow Duration  Total Length of Fwd Packets  Fwd Packet Length Max  \\\n",
       "0              50833                            0                      0   \n",
       "1                 49                            0                      0   \n",
       "2                306                            6                      6   \n",
       "3              63041                           65                     65   \n",
       "4              47682                           43                     43   \n",
       "...              ...                          ...                    ...   \n",
       "28298             45                            0                      0   \n",
       "28299      114309573                          511                    427   \n",
       "28300          48850                           80                     40   \n",
       "28301            260                           66                     33   \n",
       "28302             25                            6                      6   \n",
       "\n",
       "       Fwd Packet Length Mean  Bwd Packet Length Max  Bwd Packet Length Min  \\\n",
       "0                      0.0000                      0                      0   \n",
       "1                      0.0000                      0                      0   \n",
       "2                      6.0000                      6                      6   \n",
       "3                     65.0000                    124                    124   \n",
       "4                     43.0000                     59                     59   \n",
       "...                       ...                    ...                    ...   \n",
       "28298                  0.0000                      0                      0   \n",
       "28299                 31.9375                    746                      0   \n",
       "28300                 40.0000                     72                     72   \n",
       "28301                 33.0000                     97                     97   \n",
       "28302                  6.0000                      6                      6   \n",
       "\n",
       "       Flow IAT Mean  Flow IAT Min  Fwd IAT Min  Fwd Header Length  \\\n",
       "0       5.083300e+04         50833            0                 32   \n",
       "1       4.900000e+01            49           49                 64   \n",
       "2       3.060000e+02           306            0                 20   \n",
       "3       6.304100e+04         63041            0                 32   \n",
       "4       4.768200e+04         47682            0                 32   \n",
       "...              ...           ...          ...                ...   \n",
       "28298   4.500000e+01            45            0                 32   \n",
       "28299   3.941709e+06            94          165                332   \n",
       "28300   1.628333e+04             1           48                 64   \n",
       "28301   8.666667e+01            48           48                 40   \n",
       "28302   2.500000e+01            25            0                 20   \n",
       "\n",
       "       Bwd Header Length  Fwd Packets/s  Bwd Packets/s  Min Packet Length  \\\n",
       "0                     32      19.672260      19.672260                  0   \n",
       "1                      0   40816.326530       0.000000                  0   \n",
       "2                     20    3267.973856    3267.973856                  6   \n",
       "3                     32      15.862693      15.862693                 65   \n",
       "4                     32      20.972275      20.972275                 43   \n",
       "...                  ...            ...            ...                ...   \n",
       "28298                 32   22222.222220   22222.222220                  0   \n",
       "28299                424       0.139971       0.122474                  0   \n",
       "28300                 64      40.941658      40.941658                 40   \n",
       "28301                 40    7692.307692    7692.307692                 33   \n",
       "28302                 20   40000.000000   40000.000000                  6   \n",
       "\n",
       "       URG Flag Count  Down/Up Ratio  Init_Win_bytes_forward  \\\n",
       "0                   1              1                     319   \n",
       "1                   0              0                     277   \n",
       "2                   0              1                       0   \n",
       "3                   0              1                      -1   \n",
       "4                   0              1                      -1   \n",
       "...               ...            ...                     ...   \n",
       "28298               1              1                     349   \n",
       "28299               0              0                    8192   \n",
       "28300               0              1                      -1   \n",
       "28301               0              1                      -1   \n",
       "28302               1              1                   11256   \n",
       "\n",
       "       Init_Win_bytes_backward  min_seg_size_forward  Label  \n",
       "0                          153                    32      0  \n",
       "1                           -1                    32      0  \n",
       "2                            0                    20      0  \n",
       "3                           -1                    32      0  \n",
       "4                           -1                    32      0  \n",
       "...                        ...                   ...    ...  \n",
       "28298                      307                    32      0  \n",
       "28299                      343                    20      0  \n",
       "28300                       -1                    32      0  \n",
       "28301                       -1                    20      0  \n",
       "28302                    35000                    20      0  \n",
       "\n",
       "[28303 rows x 20 columns]"
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     },
     "execution_count": 4,
     "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=Auto_Encoding(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Imputation\n",
    "Detect and impute missing values to improve data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the automated data imputation function\n",
    "def Auto_Imputation(df):\n",
    "    if df.isnull().values.any() or np.isinf(df).values.any(): # if there is any empty or infinite values\n",
    "        df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "        df.fillna(0, inplace = True)  # Replace empty values with zeros; there are other imputation methods discussed in the paper\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=Auto_Imputation(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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.076, 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": [
    "# 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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Bwd Packet Length Max</th>\n",
       "      <th>Bwd Packet Length Min</th>\n",
       "      <th>Flow IAT Mean</th>\n",
       "      <th>Flow IAT Min</th>\n",
       "      <th>Fwd IAT Min</th>\n",
       "      <th>Fwd Header Length</th>\n",
       "      <th>Fwd Packets/s</th>\n",
       "      <th>Bwd Packets/s</th>\n",
       "      <th>Min Packet Length</th>\n",
       "      <th>URG Flag Count</th>\n",
       "      <th>Down/Up Ratio</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.236419e-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.707129e-04</td>\n",
       "      <td>4.707129e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>6.557420e-06</td>\n",
       "      <td>1.311484e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.002350</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.416669e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.907407e-07</td>\n",
       "      <td>4.907407e-07</td>\n",
       "      <td>4.083333e-07</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>1.360544e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.583334e-06</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>2.870370e-06</td>\n",
       "      <td>2.870370e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1.089325e-03</td>\n",
       "      <td>2.178649e-03</td>\n",
       "      <td>0.017804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.253752e-04</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.002619</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.010660</td>\n",
       "      <td>0.085635</td>\n",
       "      <td>5.837500e-04</td>\n",
       "      <td>5.837500e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>5.287564e-06</td>\n",
       "      <td>1.057513e-05</td>\n",
       "      <td>0.192878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.973835e-04</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.011151</td>\n",
       "      <td>0.005072</td>\n",
       "      <td>0.040746</td>\n",
       "      <td>4.415370e-04</td>\n",
       "      <td>4.415370e-04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>6.990758e-06</td>\n",
       "      <td>1.398152e-05</td>\n",
       "      <td>0.127596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28298</th>\n",
       "      <td>4.083335e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.537037e-07</td>\n",
       "      <td>4.537037e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>7.407407e-03</td>\n",
       "      <td>1.481481e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.005341</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28299</th>\n",
       "      <td>9.525802e-01</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.017204</td>\n",
       "      <td>0.008282</td>\n",
       "      <td>0.064133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.649735e-02</td>\n",
       "      <td>9.074074e-07</td>\n",
       "      <td>1.375000e-06</td>\n",
       "      <td>0.001554</td>\n",
       "      <td>4.665693e-08</td>\n",
       "      <td>8.164962e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125015</td>\n",
       "      <td>0.005249</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28300</th>\n",
       "      <td>4.071168e-04</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.001612</td>\n",
       "      <td>0.010373</td>\n",
       "      <td>0.006190</td>\n",
       "      <td>0.049724</td>\n",
       "      <td>1.508086e-04</td>\n",
       "      <td>4.629629e-08</td>\n",
       "      <td>4.000000e-07</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>1.364722e-05</td>\n",
       "      <td>2.729444e-05</td>\n",
       "      <td>0.118694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28301</th>\n",
       "      <td>2.200001e-06</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.008558</td>\n",
       "      <td>0.008339</td>\n",
       "      <td>0.066989</td>\n",
       "      <td>8.395061e-07</td>\n",
       "      <td>4.814815e-07</td>\n",
       "      <td>4.000000e-07</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>2.564103e-03</td>\n",
       "      <td>5.128205e-03</td>\n",
       "      <td>0.097923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28302</th>\n",
       "      <td>2.416668e-07</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>2.685185e-07</td>\n",
       "      <td>2.685185e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1.333333e-02</td>\n",
       "      <td>2.666667e-02</td>\n",
       "      <td>0.017804</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.171768</td>\n",
       "      <td>0.534073</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28303 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flow Duration  Total Length of Fwd Packets  Fwd Packet Length Max  \\\n",
       "0       4.236419e-04                     0.000000               0.000000   \n",
       "1       4.416669e-07                     0.000000               0.000000   \n",
       "2       2.583334e-06                     0.000008               0.000242   \n",
       "3       5.253752e-04                     0.000090               0.002619   \n",
       "4       3.973835e-04                     0.000060               0.001732   \n",
       "...              ...                          ...                    ...   \n",
       "28298   4.083335e-07                     0.000000               0.000000   \n",
       "28299   9.525802e-01                     0.000710               0.017204   \n",
       "28300   4.071168e-04                     0.000111               0.001612   \n",
       "28301   2.200001e-06                     0.000092               0.001330   \n",
       "28302   2.416668e-07                     0.000008               0.000242   \n",
       "\n",
       "       Fwd Packet Length Mean  Bwd Packet Length Max  Bwd Packet Length Min  \\\n",
       "0                    0.000000               0.000000               0.000000   \n",
       "1                    0.000000               0.000000               0.000000   \n",
       "2                    0.001556               0.000516               0.004144   \n",
       "3                    0.016856               0.010660               0.085635   \n",
       "4                    0.011151               0.005072               0.040746   \n",
       "...                       ...                    ...                    ...   \n",
       "28298                0.000000               0.000000               0.000000   \n",
       "28299                0.008282               0.064133               0.000000   \n",
       "28300                0.010373               0.006190               0.049724   \n",
       "28301                0.008558               0.008339               0.066989   \n",
       "28302                0.001556               0.000516               0.004144   \n",
       "\n",
       "       Flow IAT Mean  Flow IAT Min   Fwd IAT Min  Fwd Header Length  \\\n",
       "0       4.707129e-04  4.707129e-04  0.000000e+00           0.000150   \n",
       "1       4.907407e-07  4.907407e-07  4.083333e-07           0.000299   \n",
       "2       2.870370e-06  2.870370e-06  0.000000e+00           0.000094   \n",
       "3       5.837500e-04  5.837500e-04  0.000000e+00           0.000150   \n",
       "4       4.415370e-04  4.415370e-04  0.000000e+00           0.000150   \n",
       "...              ...           ...           ...                ...   \n",
       "28298   4.537037e-07  4.537037e-07  0.000000e+00           0.000150   \n",
       "28299   3.649735e-02  9.074074e-07  1.375000e-06           0.001554   \n",
       "28300   1.508086e-04  4.629629e-08  4.000000e-07           0.000299   \n",
       "28301   8.395061e-07  4.814815e-07  4.000000e-07           0.000187   \n",
       "28302   2.685185e-07  2.685185e-07  0.000000e+00           0.000094   \n",
       "\n",
       "       Fwd Packets/s  Bwd Packets/s  Min Packet Length  URG Flag Count  \\\n",
       "0       6.557420e-06   1.311484e-05           0.000000             1.0   \n",
       "1       1.360544e-02   0.000000e+00           0.000000             0.0   \n",
       "2       1.089325e-03   2.178649e-03           0.017804             0.0   \n",
       "3       5.287564e-06   1.057513e-05           0.192878             0.0   \n",
       "4       6.990758e-06   1.398152e-05           0.127596             0.0   \n",
       "...              ...            ...                ...             ...   \n",
       "28298   7.407407e-03   1.481481e-02           0.000000             1.0   \n",
       "28299   4.665693e-08   8.164962e-08           0.000000             0.0   \n",
       "28300   1.364722e-05   2.729444e-05           0.118694             0.0   \n",
       "28301   2.564103e-03   5.128205e-03           0.097923             0.0   \n",
       "28302   1.333333e-02   2.666667e-02           0.017804             1.0   \n",
       "\n",
       "       Down/Up Ratio  Init_Win_bytes_forward  Init_Win_bytes_backward  \\\n",
       "0           0.142857                0.004883                 0.002350   \n",
       "1           0.000000                0.004242                 0.000000   \n",
       "2           0.142857                0.000015                 0.000015   \n",
       "3           0.142857                0.000000                 0.000000   \n",
       "4           0.142857                0.000000                 0.000000   \n",
       "...              ...                     ...                      ...   \n",
       "28298       0.142857                0.005341                 0.004700   \n",
       "28299       0.000000                0.125015                 0.005249   \n",
       "28300       0.142857                0.000000                 0.000000   \n",
       "28301       0.142857                0.000000                 0.000000   \n",
       "28302       0.142857                0.171768                 0.534073   \n",
       "\n",
       "       min_seg_size_forward  Label  \n",
       "0                  0.571429      0  \n",
       "1                  0.571429      0  \n",
       "2                  0.357143      0  \n",
       "3                  0.571429      0  \n",
       "4                  0.571429      0  \n",
       "...                     ...    ...  \n",
       "28298              0.571429      0  \n",
       "28299              0.357143      0  \n",
       "28300              0.571429      0  \n",
       "28301              0.357143      0  \n",
       "28302              0.357143      0  \n",
       "\n",
       "[28303 rows x 19 columns]"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    180\n",
       "1    103\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = Auto_Balancing(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    180\n",
       "1    180\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.99      0.91     22482\n",
      "           1       0.91      0.21      0.34      5538\n",
      "\n",
      "    accuracy                           0.84     28020\n",
      "   macro avg       0.87      0.60      0.62     28020\n",
      "weighted avg       0.85      0.84      0.80     28020\n",
      "\n",
      "Accuracy: 83.958%\n",
      "Precision: 90.902%\n",
      "Recall: 20.928%\n",
      "F1-score: 34.022999999999996%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 254 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": 21,
   "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": 22,
   "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": 23,
   "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 CICIDS2017 dataset', fontsize=15)\n",
    "    plt.xlabel('Number of samples')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "\n",
    "    plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.827%\n",
      "Precision: 77.806%\n",
      "Recall: 89.13%\n",
      "F1-score: 83.084%\n",
      "Wall time: 6.89 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.483%\n",
      "Precision: 94.562%\n",
      "Recall: 97.96000000000001%\n",
      "F1-score: 96.231%\n",
      "Wall time: 10.6 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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.87599999999999%\n",
      "Precision: 97.612%\n",
      "Recall: 96.678%\n",
      "F1-score: 97.142%\n",
      "Wall time: 17.5 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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.997%\n",
      "Precision: 98.274%\n",
      "Recall: 96.623%\n",
      "F1-score: 97.442%\n",
      "Wall time: 22.4 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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.851%\n",
      "Precision: 97.626%\n",
      "Recall: 96.533%\n",
      "F1-score: 97.076%\n",
      "Wall time: 1min 8s\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_1.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.286%\n",
      "Precision: 99.335%\n",
      "Recall: 97.039%\n",
      "F1-score: 98.173%\n",
      "Wall time: 2min 47s\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": 30,
   "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 = [0,6200,10100, 15100,16700,21500,24300]\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 CICIDS2017', fontsize=40)\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "\n",
    "plt.draw()"
   ]
  }
 ],
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