rd_user_auth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENHANCING USER AUTHENTICATION EXPERIENCE THROUGH BIG DATA ANALYSIS AND SINGLE SIGN-ON INTEGRATION\n",
    "## K-MEANS ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, roc_auc_score\n",
    "from imblearn.combine import SMOTETomek\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report, roc_curve, auc, accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../rba-dataset/rba-dataset.csv', nrows=2000, index_col='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'xgb__subsample': 0.7, 'xgb__n_estimators': 50, 'xgb__max_depth': 3, 'xgb__learning_rate': 0.1, 'xgb__colsample_bytree': 0.8}\n",
      "Accuracy: 0.7058823529411765\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.50      0.40      0.44         5\n",
      "        True       0.77      0.83      0.80        12\n",
      "\n",
      "    accuracy                           0.71        17\n",
      "   macro avg       0.63      0.62      0.62        17\n",
      "weighted avg       0.69      0.71      0.70        17\n",
      "\n",
      "ROC-AUC Score: 0.6333333333333333\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "data['Login Timestamp'] = pd.to_datetime(data['Login Timestamp'])\n",
    "data['hour'] = data['Login Timestamp'].dt.hour\n",
    "data['day_of_week'] = data['Login Timestamp'].dt.dayofweek\n",
    "data['is_weekend'] = data['Login Timestamp'].dt.weekday >= 5\n",
    "\n",
    "# New feature: Number of unique IP addresses per user\n",
    "data['unique_ips'] = data.groupby('User ID')['IP Address'].transform('nunique')\n",
    "\n",
    "features = [\n",
    "    'User ID', 'Round-Trip Time [ms]', 'unique_ips', 'Country', 'Region', \n",
    "    'City', 'ASN', 'User Agent String', 'Browser Name and Version', \n",
    "    'OS Name and Version', 'Device Type', 'Is Attack IP', 'Is Account Takeover',\n",
    "    'hour', 'day_of_week', 'is_weekend'\n",
    "]\n",
    "target = 'Login Successful'\n",
    "\n",
    "X = data[features]\n",
    "y = data[target]\n",
    "\n",
    "categorical_features = ['Country', 'Region', 'City', 'User Agent String',\n",
    "                        'Browser Name and Version', 'OS Name and Version', 'Device Type']\n",
    "numerical_features = ['User ID', 'Round-Trip Time [ms]', 'ASN', 'hour', 'day_of_week', 'is_weekend', 'unique_ips']\n",
    "\n",
    "# Preprocessing pipelines for both numeric and categorical features\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numerical_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ])\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Preprocess the training data\n",
    "X_train_preprocessed = preprocessor.fit_transform(X_train)\n",
    "X_test_preprocessed = preprocessor.transform(X_test)\n",
    "\n",
    "# Apply SMOTETomek to the preprocessed training data\n",
    "smote_tomek = SMOTETomek(random_state=42)\n",
    "X_train_res, y_train_res = smote_tomek.fit_resample(X_train_preprocessed, y_train)\n",
    "\n",
    "# Define base models for stacking\n",
    "estimators = [\n",
    "    ('rf', RandomForestClassifier(random_state=42)),\n",
    "    ('xgb', XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42))\n",
    "]\n",
    "\n",
    "# Stacking Classifier\n",
    "stacking_clf = StackingClassifier(\n",
    "    estimators=estimators,\n",
    "    final_estimator=XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42)\n",
    ")\n",
    "\n",
    "# Hyperparameter tuning for the stacking classifier\n",
    "param_grid = {\n",
    "    'xgb__n_estimators': [50, 100, 200],\n",
    "    'xgb__max_depth': [3, 6, 9],\n",
    "    'xgb__learning_rate': [0.01, 0.1, 0.2],\n",
    "    'xgb__subsample': [0.7, 0.8, 1.0],\n",
    "    'xgb__colsample_bytree': [0.7, 0.8, 1.0]\n",
    "}\n",
    "\n",
    "random_search = RandomizedSearchCV(stacking_clf, param_distributions=param_grid, n_iter=50, cv=3, scoring='roc_auc', n_jobs=-1, random_state=42)\n",
    "random_search.fit(X_train_res, y_train_res)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_pred = random_search.predict(X_test_preprocessed)\n",
    "print('Best Parameters:', random_search.best_params_)\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print('Classification Report:', classification_report(y_test, y_pred))\n",
    "print('ROC-AUC Score:', roc_auc_score(y_test, random_search.predict_proba(X_test_preprocessed)[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy Bar Chart\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "\n",
    "# Create a pie chart with a hole in the middle (donut chart)\n",
    "wedges, texts = ax.pie([accuracy, 1-accuracy], \n",
    "                       radius=1.2, \n",
    "                       colors=['skyblue', 'lightgray'], \n",
    "                       startangle=90, \n",
    "                       wedgeprops=dict(width=0.3, edgecolor='w'))\n",
    "\n",
    "# Add a center circle\n",
    "centre_circle = plt.Circle((0, 0), 0.70, fc='white')\n",
    "fig.gca().add_artist(centre_circle)\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax.set_title('Model Accuracy', fontsize=16)\n",
    "\n",
    "# Add text in the middle\n",
    "plt.text(0, 0, f'{accuracy:.2%}', horizontalalignment='center', verticalalignment='center', fontsize=20)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Classification Report Metrics\n",
    "report = classification_report(y_test, y_pred, output_dict=True)\n",
    "report_df = pd.DataFrame(report).transpose()\n",
    "report_df = report_df.drop(['accuracy', 'macro avg', 'weighted avg'], axis=0)\n",
    "\n",
    "# Plot grouped bar chart\n",
    "report_df[['precision', 'recall', 'f1-score']].plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('Classification Report Metrics')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "# ROC Curve\n",
    "y_pred_proba = random_search.predict_proba(X_test_preprocessed)[:, 1]\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['preprocessor.pkl']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the model to a file\n",
    "joblib_file = \"xgb_model.pkl\"\n",
    "joblib.dump(random_search.best_estimator_, joblib_file)\n",
    "\n",
    "# Save the fitted preprocessor\n",
    "joblib.dump(X_train_preprocessed, 'preprocessor.pkl')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "auth-insight-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}