DDoS_Feature Selection.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "data = pd.read_csv('/Users/suchithkurra/Desktop/Honey_Pot Research/data/DDoS.csv')"
   ],
   "metadata": {
    "collapsed": false,
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     "end_time": "2024-04-20T06:54:26.941819Z",
     "start_time": "2024-04-20T06:54:26.243783Z"
    }
   },
   "id": "3f2f2ada3d7cd49d"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# Data preprocessing\n",
    "# Drop any rows with missing values\n",
    "data.dropna(inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-20T06:54:27.766856Z",
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    }
   },
   "id": "22823b8243e803ea"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# Replace infinite or very large values with NaN\n",
    "data.replace([np.inf, -np.inf], np.nan, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
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     "end_time": "2024-04-20T06:54:28.396528Z",
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   },
   "id": "9bec1004784e63bb"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# Drop rows containing NaN values\n",
    "data.dropna(inplace=True)"
   ],
   "metadata": {
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     "end_time": "2024-04-20T06:54:29.020006Z",
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   "id": "ea481195fcc66ae3"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Encode target variable\n",
    "label_encoder = LabelEncoder()\n",
    "data[' Label'] = label_encoder.fit_transform(data[' Label'])\n"
   ],
   "metadata": {
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   "id": "9fe1e88ddbb1178"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# Split the dataset into features and target variable\n",
    "X = data.drop(' Label', axis=1)  # Adjust 'target_column_name' with the name of your target column\n",
    "y = data[' Label']"
   ],
   "metadata": {
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   },
   "id": "ba5db3970f6cbdf"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
   ],
   "metadata": {
    "collapsed": false,
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   "id": "4cae32d6ad9cc656"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "# Feature scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ],
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  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "# SelectKBest with ANOVA F-value\n",
    "k = 20  # Number of top features to select\n",
    "selector = SelectKBest(score_func=f_classif, k=k)\n",
    "X_train_selected = selector.fit_transform(X_train_scaled, y_train)\n",
    "X_test_selected = selector.transform(X_test_scaled)"
   ],
   "metadata": {
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   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "selected_features_indices = selector.get_support(indices=True)\n"
   ],
   "metadata": {
    "collapsed": false,
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     "end_time": "2024-04-20T06:54:35.418547Z",
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    }
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   "id": "259c9c5fede19466"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "selected_feature_names = X.columns[selected_features_indices]\n"
   ],
   "metadata": {
    "collapsed": false,
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     "end_time": "2024-04-20T06:54:36.332798Z",
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   "id": "b0f08e6238b8a1e"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features:\n",
      "Index([' Destination Port', 'Total Length of Fwd Packets',\n",
      "       ' Fwd Packet Length Max', ' Fwd Packet Length Mean',\n",
      "       'Bwd Packet Length Max', ' Bwd Packet Length Min',\n",
      "       ' Bwd Packet Length Mean', ' Bwd Packet Length Std', 'Bwd IAT Total',\n",
      "       ' Min Packet Length', ' Max Packet Length', ' Packet Length Mean',\n",
      "       ' Packet Length Std', ' Packet Length Variance', ' URG Flag Count',\n",
      "       ' Average Packet Size', ' Avg Fwd Segment Size',\n",
      "       ' Avg Bwd Segment Size', ' Subflow Fwd Bytes', ' min_seg_size_forward'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the selected feature names\n",
    "print(\"Selected features:\")\n",
    "print(selected_feature_names)"
   ],
   "metadata": {
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    "ExecuteTime": {
     "end_time": "2024-04-20T06:54:37.851453Z",
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   "id": "178bb9124c50ab10"
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   "cell_type": "code",
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train_selected, y_train)"
   ],
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    "collapsed": false,
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# Predictions\n",
    "y_pred = rf_model.predict(X_test_selected)"
   ],
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     "end_time": "2024-04-20T06:54:46.349332Z",
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   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     29321\n",
      "           1       1.00      1.00      1.00     38393\n",
      "\n",
      "    accuracy                           1.00     67714\n",
      "   macro avg       1.00      1.00      1.00     67714\n",
      "weighted avg       1.00      1.00      1.00     67714\n",
      "\n",
      "Confusion Matrix:\n",
      "[[29319     2]\n",
      " [   25 38368]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluation metrics\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_test, y_pred))"
   ],
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  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "# ROC Curve and AUC Score\n",
    "y_prob = rf_model.predict_proba(X_test_selected)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(fpr, tpr)"
   ],
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      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', 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()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-20T06:55:03.618173Z",
     "start_time": "2024-04-20T06:55:03.002408Z"
    }
   },
   "id": "4a1654e9df70ccc1"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, f1_score\n",
    "import numpy as np\n",
    "\n",
    "# Compute accuracy, precision, and f1-score for different thresholds\n",
    "thresholds = np.linspace(0, 1, 100)\n",
    "accuracies = []\n",
    "precisions = []\n",
    "f1_scores = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "    y_pred_thresholded = (y_prob > threshold).astype(int)\n",
    "    accuracies.append(accuracy_score(y_test, y_pred_thresholded))\n",
    "    precisions.append(precision_score(y_test, y_pred_thresholded))\n",
    "    f1_scores.append(f1_score(y_test, y_pred_thresholded))\n",
    "    \n",
    "    \n",
    "# print('accuracies: ' + accuracy_score)\n",
    "# print('precision: ' + precisions)\n",
    "# print('F1: ' + accuracies)\n",
    "# Plot accuracy\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(thresholds, accuracies, label='Accuracy', color='blue')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy vs. Threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot precision\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(thresholds, precisions, label='Precision', color='green')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision vs. Threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot F1-score\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(thresholds, f1_scores, label='F1-score', color='red')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('F1-score')\n",
    "plt.title('F1-score vs. Threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-20T07:00:31.182496Z",
     "start_time": "2024-04-20T07:00:27.834136Z"
    }
   },
   "id": "298c77124586e2d1"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.999601264140355\n",
      "precision: 0.9999478759447485\n",
      "f1: 0.9996482680458034\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, f1_score\n",
    "\n",
    "# Compute accuracy, precision, and F1-score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print('accuracy: ' + str(accuracy))\n",
    "print('precision: ' + str(precision))\n",
    "print('f1: ' + str(f1))\n",
    "# Plotting\n",
    "metrics = ['Accuracy', 'Precision', 'F1-score']\n",
    "values = [accuracy, precision, f1]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(metrics, values, color=['blue', 'green', 'red'])\n",
    "plt.ylabel('Score')\n",
    "plt.title('Evaluation Metrics')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-20T07:02:11.148088Z",
     "start_time": "2024-04-20T07:02:11.049267Z"
    }
   },
   "id": "ecec8c0cba476d66"
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "['random_forest_model_ddos.pkl']"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "# Save the trained model to a file\n",
    "joblib.dump(rf_model, 'random_forest_model_ddos.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-20T07:04:51.421081Z",
     "start_time": "2024-04-20T07:04:51.391787Z"
    }
   },
   "id": "8bc7806caa9c66f1"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Extracting false positives and false negatives from confusion matrix\n",
    "FP = conf_matrix[0, 1]  # False positives: predicted DDoS, actual BENIGN\n",
    "FN = conf_matrix[1, 0]  # False negatives: predicted BENIGN, actual DDoS\n",
    "\n",
    "print(\"False Positives:\", FP)\n",
    "print(\"False Negatives:\", FN)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5f3494e17efc4992"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}