classification_all.ipynb

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     "text": [
      "Requirement already satisfied: keras in /Users/christinastodt/anaconda3/envs/AnomalyTest/lib/python3.9/site-packages (2.14.0)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install keras tensorflow scikit-learn numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "981b9964-440a-40f3-81aa-1536b954baca",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Load the training and testing data\n",
    "train_data = pd.read_csv('train_LDA1.csv')\n",
    "test_data = pd.read_csv('test_LDA1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94a80eaf-d96d-4aff-9726-acbe3693f8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the features to be used for the autoencoder\n",
    "selected_features = [\n",
    "    'Dst Port', 'Fwd Packet Length Max', 'Bwd Packet Length Max',\n",
    "    'Bwd Packet Length Mean', 'Bwd Packet Length Std', 'Flow IAT Mean',\n",
    "    'Fwd IAT Std', 'Bwd IAT Mean', 'Fwd PSH Flags', 'Bwd PSH Flags',\n",
    "    'Fwd RST Flags', 'Fwd Header Length', 'Packet Length Max',\n",
    "    'Packet Length Std', 'Fwd Segment Size Avg', 'Bwd Segment Size Avg',\n",
    "    'ICMP Type'\n",
    "]\n",
    "X_train = train_data.loc[:, selected_features]\n",
    "y_train = train_data['Label']  # Assuming the last column is 'Label'\n",
    "X_test = test_data.loc[:, selected_features]\n",
    "y_test = test_data['Label']    # Assuming the last column is 'Label'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d48bba16-1be3-440e-aa3d-e2b87fe04037",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Training Data ---\n",
      "                              precision    recall  f1-score   support\n",
      "\n",
      "                      BENIGN       0.98      0.96      0.97    223383\n",
      "               DoS GoldenEye       0.54      0.45      0.49      5297\n",
      "   DoS GoldenEye - Attempted       0.00      0.00      0.00        56\n",
      "                    DoS Hulk       0.96      0.98      0.97    110927\n",
      "        DoS Hulk - Attempted       0.23      0.04      0.07       407\n",
      "            DoS Slowhttptest       0.31      0.57      0.40      1218\n",
      "DoS Slowhttptest - Attempted       0.77      0.83      0.80      2358\n",
      "               DoS Slowloris       0.38      0.72      0.50      2701\n",
      "   DoS Slowloris - Attempted       0.00      0.00      0.00      1293\n",
      "                  Heartbleed       0.08      1.00      0.14         8\n",
      "\n",
      "                    accuracy                           0.95    347648\n",
      "                   macro avg       0.42      0.56      0.43    347648\n",
      "                weighted avg       0.95      0.95      0.95    347648\n",
      "\n",
      "Training Accuracy: 0.9533695001840943\n",
      "--- Test Data ---\n",
      "                              precision    recall  f1-score   support\n",
      "\n",
      "                      BENIGN       0.98      0.96      0.97     95737\n",
      "               DoS GoldenEye       0.55      0.44      0.49      2270\n",
      "   DoS GoldenEye - Attempted       0.00      0.00      0.00        24\n",
      "                    DoS Hulk       0.96      0.98      0.97     47541\n",
      "        DoS Hulk - Attempted       0.34      0.07      0.11       174\n",
      "            DoS Slowhttptest       0.29      0.55      0.38       522\n",
      "DoS Slowhttptest - Attempted       0.76      0.83      0.80      1010\n",
      "               DoS Slowloris       0.37      0.73      0.49      1158\n",
      "   DoS Slowloris - Attempted       0.00      0.00      0.00       554\n",
      "                  Heartbleed       0.08      1.00      0.15         3\n",
      "\n",
      "                    accuracy                           0.95    148993\n",
      "                   macro avg       0.43      0.56      0.44    148993\n",
      "                weighted avg       0.95      0.95      0.95    148993\n",
      "\n",
      "Test Accuracy: 0.9526353587081272\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# Assuming train_data and test_data are already defined and preprocessed\n",
    "\n",
    "# Split the train_data into features and labels\n",
    "X_train = train_data.loc[:, selected_features]\n",
    "y_train = train_data['Label']  # 'Label' is already numerical\n",
    "\n",
    "# Split the test_data into features and labels\n",
    "X_test = test_data.loc[:, selected_features]\n",
    "y_test = test_data['Label']  # 'Label' is already numerical\n",
    "\n",
    "# Initialize and train the LDA model\n",
    "lda = LinearDiscriminantAnalysis(solver='svd')\n",
    "lda.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the training and testing data\n",
    "lda_training_predictions = lda.predict(X_train)\n",
    "lda_testing_predictions = lda.predict(X_test)\n",
    "\n",
    "# Print classification reports and accuracy scores\n",
    "print(\"--- Training Data ---\")\n",
    "print(classification_report(y_train, lda_training_predictions))\n",
    "print(\"Training Accuracy:\", accuracy_score(y_train, lda_training_predictions))\n",
    "\n",
    "print(\"--- Test Data ---\")\n",
    "print(classification_report(y_test, lda_testing_predictions))\n",
    "print(\"Test Accuracy:\", accuracy_score(y_test, lda_testing_predictions))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c513104-5fec-4608-b533-e7eb05f91bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the LDA model to a file\n",
    "with open('lda_model.pkl', 'wb') as file:\n",
    "    pickle.dump(lda, file)\n"
   ]
  }
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