{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Jo-VwCSI2CfA",
"outputId": "31ab62e4-f9a4-4e56-a57e-855423cae71a"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pandas'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Import necessary libraries\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m pandas \u001b[38;5;28;01mas\u001b[39;00m pd\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m matplotlib.pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m seaborn \u001b[38;5;28;01mas\u001b[39;00m sns\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'"
]
}
],
"source": [
"# Import necessary libraries\n",
"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.preprocessing import StandardScaler, LabelEncoder\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization, Reshape\n",
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
"from tensorflow.keras.optimizers import Adam\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Set random seeds for reproducibility\n",
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"print(\"Libraries imported successfully!\")\n",
"print(f\"TensorFlow version: {tf.__version__}\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5-Gi5Nx02Eyi",
"outputId": "5b0b4907-8a42-4866-e109-46d8d0bd3368"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading dataset...\n",
"Dataset shape: (10000, 17)\n",
"Attack distribution:\n",
"attack_type\n",
"Normal 8030\n",
"Brute Force 515\n",
"DoS 500\n",
"DDoS 487\n",
"Port Scan 468\n",
"Name: count, dtype: int64\n",
"Training set size: 8000\n",
"Test set size: 2000\n",
"Attack distribution in training:\n",
"0 6424\n",
"1 1576\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Create sample dataset for demonstration\n",
"def create_sample_dataset():\n",
" \"\"\"\n",
" Create a sample dataset for demonstration purposes\n",
" \"\"\"\n",
" np.random.seed(42)\n",
" n_samples = 10000\n",
"\n",
" # Generate synthetic features\n",
" features = pd.DataFrame({\n",
" 'duration': np.random.exponential(100, n_samples),\n",
" 'protocol_type': np.random.choice(['tcp', 'udp', 'icmp'], n_samples),\n",
" 'service': np.random.choice(['http', 'ftp', 'smtp', 'ssh', 'dns', 'https'], n_samples),\n",
" 'flag': np.random.choice(['SF', 'S0', 'REJ', 'RSTO', 'RSTR'], n_samples),\n",
" 'src_bytes': np.random.exponential(1000, n_samples),\n",
" 'dst_bytes': np.random.exponential(500, n_samples),\n",
" 'count': np.random.poisson(10, n_samples),\n",
" 'srv_count': np.random.poisson(5, n_samples),\n",
" 'serror_rate': np.random.beta(1, 10, n_samples),\n",
" 'srv_serror_rate': np.random.beta(1, 10, n_samples),\n",
" 'rerror_rate': np.random.beta(1, 10, n_samples),\n",
" 'srv_rerror_rate': np.random.beta(1, 10, n_samples),\n",
" 'same_srv_rate': np.random.beta(2, 5, n_samples),\n",
" 'diff_srv_rate': np.random.beta(1, 5, n_samples),\n",
" 'dst_host_count': np.random.poisson(20, n_samples),\n",
" })\n",
"\n",
" # Generate labels (0: normal, 1: attack)\n",
" labels = np.random.choice([0, 1], n_samples, p=[0.8, 0.2])\n",
"\n",
" # Add some pattern to make it more realistic\n",
" attack_indices = np.where(labels == 1)[0]\n",
" for idx in attack_indices:\n",
" features.loc[idx, 'src_bytes'] *= np.random.uniform(2, 5)\n",
" features.loc[idx, 'dst_bytes'] *= np.random.uniform(1.5, 4)\n",
"\n",
" # Add attack types\n",
" attack_types = ['DoS', 'DDoS', 'Brute Force', 'Port Scan', 'Normal']\n",
" attack_labels = []\n",
" for label in labels:\n",
" if label == 0:\n",
" attack_labels.append('Normal')\n",
" else:\n",
" attack_labels.append(np.random.choice(['DoS', 'DDoS', 'Brute Force', 'Port Scan']))\n",
"\n",
" features['label'] = labels\n",
" features['attack_type'] = attack_labels\n",
"\n",
" return features\n",
"\n",
"# Function to preprocess data\n",
"def preprocess_data(data, is_training=True, scaler=None, label_encoders=None):\n",
" \"\"\"\n",
" Preprocess the dataset for model training\n",
" \"\"\"\n",
" df = data.copy()\n",
"\n",
" # Handle missing values\n",
" df = df.dropna()\n",
"\n",
" # Separate features and labels\n",
" if 'label' in df.columns:\n",
" y = df['label'].values\n",
" X = df.drop(['label', 'attack_type'], axis=1, errors='ignore')\n",
" else:\n",
" X = df\n",
" y = None\n",
"\n",
" # Identify categorical columns\n",
" categorical_cols = X.select_dtypes(include=['object']).columns.tolist()\n",
"\n",
" # Encode categorical variables\n",
" if is_training:\n",
" label_encoders = {}\n",
" for col in categorical_cols:\n",
" le = LabelEncoder()\n",
" X[col] = le.fit_transform(X[col].astype(str))\n",
" label_encoders[col] = le\n",
" else:\n",
" for col in categorical_cols:\n",
" if col in label_encoders:\n",
" X[col] = label_encoders[col].transform(X[col].astype(str))\n",
" else:\n",
" # Handle unseen categories\n",
" le = LabelEncoder()\n",
" X[col] = le.fit_transform(X[col].astype(str))\n",
" label_encoders[col] = le\n",
"\n",
" # Convert all columns to numeric\n",
" for col in X.columns:\n",
" X[col] = pd.to_numeric(X[col], errors='coerce')\n",
"\n",
" # Handle any remaining NaN values\n",
" X = X.fillna(0)\n",
"\n",
" # Scale numerical features\n",
" if is_training:\n",
" scaler = StandardScaler()\n",
" X_scaled = scaler.fit_transform(X)\n",
" else:\n",
" X_scaled = scaler.transform(X)\n",
"\n",
" return X_scaled, y, scaler, label_encoders\n",
"\n",
"# Load and preprocess data\n",
"print(\"Loading dataset...\")\n",
"data = create_sample_dataset()\n",
"print(f\"Dataset shape: {data.shape}\")\n",
"print(f\"Attack distribution:\\n{data['attack_type'].value_counts()}\")\n",
"\n",
"# Preprocess the data\n",
"X, y, scaler, label_encoders = preprocess_data(data, is_training=True)\n",
"\n",
"# Split into train and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
"\n",
"print(f\"Training set size: {len(X_train)}\")\n",
"print(f\"Test set size: {len(X_test)}\")\n",
"print(f\"Attack distribution in training:\\n{pd.Series(y_train).value_counts()}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "To1_eXuW2E4x",
"outputId": "38d081fc-ac78-4472-edf2-b3dad661e6d3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANN Model Summary:\n"
]
},
{
"data": {
"text/html": [
"Model: \"sequential_2\"\n",
"\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ dense_6 (Dense) │ (None, 128) │ 2,048 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_4 │ (None, 128) │ 512 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_7 (Dropout) │ (None, 128) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_7 (Dense) │ (None, 64) │ 8,256 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_5 │ (None, 64) │ 256 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_8 (Dropout) │ (None, 64) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_8 (Dense) │ (None, 32) │ 2,080 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_6 │ (None, 32) │ 128 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_9 (Dropout) │ (None, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_9 (Dense) │ (None, 16) │ 528 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_7 │ (None, 16) │ 64 │\n",
"│ (BatchNormalization) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_10 (Dropout) │ (None, 16) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_10 (Dense) │ (None, 1) │ 17 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
"\n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m528\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ batch_normalization_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m17\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Total params: 13,889 (54.25 KB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m13,889\u001b[0m (54.25 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Trainable params: 13,409 (52.38 KB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,409\u001b[0m (52.38 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Non-trainable params: 480 (1.88 KB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m480\u001b[0m (1.88 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"LSTM Model Summary:\n"
]
},
{
"data": {
"text/html": [
"Model: \"sequential_3\"\n",
"\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ reshape_1 (Reshape) │ (None, 15, 1) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_3 (LSTM) │ (None, 15, 64) │ 16,896 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_11 (Dropout) │ (None, 15, 64) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_4 (LSTM) │ (None, 15, 32) │ 12,416 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_12 (Dropout) │ (None, 15, 32) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_5 (LSTM) │ (None, 16) │ 3,136 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_13 (Dropout) │ (None, 16) │ 0 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_11 (Dense) │ (None, 1) │ 17 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
"\n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ reshape_1 (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,896\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m12,416\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_12 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m3,136\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m17\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Total params: 32,465 (126.82 KB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m32,465\u001b[0m (126.82 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Trainable params: 32,465 (126.82 KB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m32,465\u001b[0m (126.82 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" Non-trainable params: 0 (0.00 B)\n",
"\n"
],
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def build_ann_model(input_shape, num_classes=1):\n",
" \"\"\"\n",
" Build an Artificial Neural Network model for binary classification\n",
" \"\"\"\n",
" model = Sequential([\n",
" Dense(128, activation='relu', input_shape=(input_shape,)),\n",
" BatchNormalization(),\n",
" Dropout(0.3),\n",
"\n",
" Dense(64, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.3),\n",
"\n",
" Dense(32, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.2),\n",
"\n",
" Dense(16, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(0.2),\n",
"\n",
" Dense(num_classes, activation='sigmoid')\n",
" ])\n",
"\n",
" # FIXED: Use custom metrics with proper names\n",
" model.compile(\n",
" optimizer=Adam(learning_rate=0.001),\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy', 'Precision', 'Recall'] # Capitalized metric names\n",
" )\n",
"\n",
" return model\n",
"\n",
"def build_lstm_model(input_shape, num_classes=1):\n",
" \"\"\"\n",
" Build an LSTM model for binary classification\n",
" \"\"\"\n",
" model = Sequential([\n",
" Reshape((input_shape, 1), input_shape=(input_shape,)),\n",
" LSTM(64, return_sequences=True),\n",
" Dropout(0.3),\n",
" LSTM(32, return_sequences=True),\n",
" Dropout(0.3),\n",
" LSTM(16),\n",
" Dropout(0.2),\n",
" Dense(num_classes, activation='sigmoid')\n",
" ])\n",
"\n",
" # FIXED: Use custom metrics with proper names\n",
" model.compile(\n",
" optimizer=Adam(learning_rate=0.001),\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy', 'Precision', 'Recall'] # Capitalized metric names\n",
" )\n",
"\n",
" return model\n",
"\n",
"# Build models\n",
"ann_model = build_ann_model(X_train.shape[1])\n",
"lstm_model = build_lstm_model(X_train.shape[1])\n",
"\n",
"print(\"ANN Model Summary:\")\n",
"ann_model.summary()\n",
"print(\"\\nLSTM Model Summary:\")\n",
"lstm_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yjdFVY9g2E8B",
"outputId": "2d5a580f-7de4-43ca-fccd-9a3b802c04ae"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training ANN Model...\n",
"Epoch 1/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - Precision: 0.3367 - Recall: 0.5821 - accuracy: 0.6905 - loss: 0.6332 - val_Precision: 0.8462 - val_Recall: 0.2839 - val_accuracy: 0.8512 - val_loss: 0.4249 - learning_rate: 0.0010\n",
"Epoch 2/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.5660 - Recall: 0.5284 - accuracy: 0.8266 - loss: 0.4467 - val_Precision: 0.8151 - val_Recall: 0.3129 - val_accuracy: 0.8531 - val_loss: 0.3808 - learning_rate: 0.0010\n",
"Epoch 3/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Precision: 0.7039 - Recall: 0.4826 - accuracy: 0.8575 - loss: 0.4025 - val_Precision: 0.7895 - val_Recall: 0.3387 - val_accuracy: 0.8544 - val_loss: 0.3769 - learning_rate: 0.0010\n",
"Epoch 4/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7414 - Recall: 0.4597 - accuracy: 0.8614 - loss: 0.3859 - val_Precision: 0.7786 - val_Recall: 0.3516 - val_accuracy: 0.8550 - val_loss: 0.3751 - learning_rate: 0.0010\n",
"Epoch 5/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7751 - Recall: 0.4518 - accuracy: 0.8656 - loss: 0.3723 - val_Precision: 0.7793 - val_Recall: 0.3645 - val_accuracy: 0.8569 - val_loss: 0.3726 - learning_rate: 0.0010\n",
"Epoch 6/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7940 - Recall: 0.4597 - accuracy: 0.8695 - loss: 0.3609 - val_Precision: 0.7687 - val_Recall: 0.3645 - val_accuracy: 0.8556 - val_loss: 0.3715 - learning_rate: 0.0010\n",
"Epoch 7/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7840 - Recall: 0.4558 - accuracy: 0.8675 - loss: 0.3665 - val_Precision: 0.7616 - val_Recall: 0.3710 - val_accuracy: 0.8556 - val_loss: 0.3702 - learning_rate: 0.0010\n",
"Epoch 8/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7838 - Recall: 0.4668 - accuracy: 0.8691 - loss: 0.3547 - val_Precision: 0.7484 - val_Recall: 0.3839 - val_accuracy: 0.8556 - val_loss: 0.3669 - learning_rate: 0.0010\n",
"Epoch 9/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - Precision: 0.7873 - Recall: 0.4589 - accuracy: 0.8684 - loss: 0.3518 - val_Precision: 0.7419 - val_Recall: 0.3710 - val_accuracy: 0.8531 - val_loss: 0.3641 - learning_rate: 0.0010\n",
"Epoch 10/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - Precision: 0.7995 - Recall: 0.4597 - accuracy: 0.8703 - loss: 0.3513 - val_Precision: 0.7533 - val_Recall: 0.3645 - val_accuracy: 0.8537 - val_loss: 0.3637 - learning_rate: 0.0010\n",
"Epoch 11/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - Precision: 0.7924 - Recall: 0.4795 - accuracy: 0.8722 - loss: 0.3463 - val_Precision: 0.7516 - val_Recall: 0.3710 - val_accuracy: 0.8544 - val_loss: 0.3655 - learning_rate: 0.0010\n",
"Epoch 12/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Precision: 0.7923 - Recall: 0.4700 - accuracy: 0.8708 - loss: 0.3456 - val_Precision: 0.7548 - val_Recall: 0.3774 - val_accuracy: 0.8556 - val_loss: 0.3654 - learning_rate: 0.0010\n",
"Epoch 13/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7975 - Recall: 0.4542 - accuracy: 0.8692 - loss: 0.3431 - val_Precision: 0.7703 - val_Recall: 0.3677 - val_accuracy: 0.8562 - val_loss: 0.3644 - learning_rate: 0.0010\n",
"Epoch 14/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.8019 - Recall: 0.4700 - accuracy: 0.8722 - loss: 0.3440 - val_Precision: 0.7516 - val_Recall: 0.3806 - val_accuracy: 0.8556 - val_loss: 0.3663 - learning_rate: 0.0010\n",
"Epoch 15/50\n",
"\u001b[1m 92/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - Precision: 0.8220 - Recall: 0.4440 - accuracy: 0.8632 - loss: 0.3562\n",
"Epoch 15: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7955 - Recall: 0.4763 - accuracy: 0.8722 - loss: 0.3388 - val_Precision: 0.7582 - val_Recall: 0.3742 - val_accuracy: 0.8556 - val_loss: 0.3671 - learning_rate: 0.0010\n",
"Epoch 16/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Precision: 0.8095 - Recall: 0.4866 - accuracy: 0.8758 - loss: 0.3349 - val_Precision: 0.7451 - val_Recall: 0.3677 - val_accuracy: 0.8531 - val_loss: 0.3663 - learning_rate: 5.0000e-04\n",
"Epoch 17/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.8032 - Recall: 0.4803 - accuracy: 0.8739 - loss: 0.3355 - val_Precision: 0.7667 - val_Recall: 0.3710 - val_accuracy: 0.8562 - val_loss: 0.3652 - learning_rate: 5.0000e-04\n",
"Epoch 18/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - Precision: 0.7987 - Recall: 0.4858 - accuracy: 0.8741 - loss: 0.3320 - val_Precision: 0.7687 - val_Recall: 0.3645 - val_accuracy: 0.8556 - val_loss: 0.3660 - learning_rate: 5.0000e-04\n",
"Epoch 19/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.8008 - Recall: 0.4763 - accuracy: 0.8730 - loss: 0.3352 - val_Precision: 0.7342 - val_Recall: 0.3742 - val_accuracy: 0.8525 - val_loss: 0.3669 - learning_rate: 5.0000e-04\n",
"Epoch 20/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - Precision: 0.8063 - Recall: 0.4497 - accuracy: 0.8625 - loss: 0.3477\n",
"Epoch 20: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - Precision: 0.7866 - Recall: 0.4747 - accuracy: 0.8706 - loss: 0.3341 - val_Precision: 0.7484 - val_Recall: 0.3742 - val_accuracy: 0.8544 - val_loss: 0.3674 - learning_rate: 5.0000e-04\n",
"Epoch 20: early stopping\n",
"Restoring model weights from the end of the best epoch: 10.\n",
"\n",
"Training LSTM Model...\n",
"Epoch 1/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 44ms/step - Precision: 0.5570 - Recall: 0.1311 - accuracy: 0.8075 - loss: 0.4924 - val_Precision: 0.6279 - val_Recall: 0.0871 - val_accuracy: 0.8131 - val_loss: 0.4606 - learning_rate: 0.0010\n",
"Epoch 2/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - Precision: 0.7070 - Recall: 0.2536 - accuracy: 0.8316 - loss: 0.4272 - val_Precision: 0.7126 - val_Recall: 0.2000 - val_accuracy: 0.8294 - val_loss: 0.4324 - learning_rate: 0.0010\n",
"Epoch 3/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - Precision: 0.7679 - Recall: 0.3136 - accuracy: 0.8455 - loss: 0.4019 - val_Precision: 0.6923 - val_Recall: 0.2903 - val_accuracy: 0.8375 - val_loss: 0.4202 - learning_rate: 0.0010\n",
"Epoch 4/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - Precision: 0.7632 - Recall: 0.3436 - accuracy: 0.8491 - loss: 0.3898 - val_Precision: 0.7377 - val_Recall: 0.2903 - val_accuracy: 0.8425 - val_loss: 0.4104 - learning_rate: 0.0010\n",
"Epoch 5/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - Precision: 0.8249 - Recall: 0.3597 - accuracy: 0.8509 - loss: 0.3976\n",
"Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 39ms/step - Precision: 0.8092 - Recall: 0.3618 - accuracy: 0.8569 - loss: 0.3833 - val_Precision: 0.7172 - val_Recall: 0.3355 - val_accuracy: 0.8456 - val_loss: 0.4029 - learning_rate: 0.0010\n",
"Epoch 6/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 27ms/step - Precision: 0.8148 - Recall: 0.3823 - accuracy: 0.8606 - loss: 0.3747 - val_Precision: 0.7205 - val_Recall: 0.3742 - val_accuracy: 0.8506 - val_loss: 0.3980 - learning_rate: 5.0000e-04\n",
"Epoch 7/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - Precision: 0.8103 - Recall: 0.4084 - accuracy: 0.8641 - loss: 0.3696 - val_Precision: 0.7052 - val_Recall: 0.3935 - val_accuracy: 0.8506 - val_loss: 0.3924 - learning_rate: 5.0000e-04\n",
"Epoch 8/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 26ms/step - Precision: 0.8055 - Recall: 0.4155 - accuracy: 0.8645 - loss: 0.3646 - val_Precision: 0.7532 - val_Recall: 0.3839 - val_accuracy: 0.8562 - val_loss: 0.3877 - learning_rate: 5.0000e-04\n",
"Epoch 9/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - Precision: 0.8134 - Recall: 0.4131 - accuracy: 0.8652 - loss: 0.3631 - val_Precision: 0.7453 - val_Recall: 0.3871 - val_accuracy: 0.8556 - val_loss: 0.3840 - learning_rate: 5.0000e-04\n",
"Epoch 10/50\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - Precision: 0.8038 - Recall: 0.4081 - accuracy: 0.8559 - loss: 0.3812\n",
"Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step - Precision: 0.8097 - Recall: 0.4100 - accuracy: 0.8642 - loss: 0.3653 - val_Precision: 0.7468 - val_Recall: 0.3806 - val_accuracy: 0.8550 - val_loss: 0.3828 - learning_rate: 5.0000e-04\n",
"Epoch 10: early stopping\n",
"Restoring model weights from the end of the best epoch: 1.\n"
]
}
],
"source": [
"# Callbacks for training\n",
"early_stopping = EarlyStopping(\n",
" monitor='val_loss',\n",
" patience=10,\n",
" restore_best_weights=True,\n",
" verbose=1\n",
")\n",
"\n",
"reduce_lr = ReduceLROnPlateau(\n",
" monitor='val_loss',\n",
" factor=0.5,\n",
" patience=5,\n",
" min_lr=0.00001,\n",
" verbose=1\n",
")\n",
"\n",
"# Train ANN model\n",
"print(\"Training ANN Model...\")\n",
"ann_history = ann_model.fit(\n",
" X_train, y_train,\n",
" epochs=50,\n",
" batch_size=64,\n",
" validation_split=0.2,\n",
" callbacks=[early_stopping, reduce_lr],\n",
" verbose=1\n",
")\n",
"\n",
"# Train LSTM model\n",
"print(\"\\nTraining LSTM Model...\")\n",
"lstm_history = lstm_model.fit(\n",
" X_train, y_train,\n",
" epochs=50,\n",
" batch_size=64,\n",
" validation_split=0.2,\n",
" callbacks=[early_stopping, reduce_lr],\n",
" verbose=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "TltijqC22E_k",
"outputId": "c24e61a5-fb86-48ce-eb74-dcfb65d54311"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
"\n",
"ANN Performance Metrics:\n",
"Accuracy: 0.8780\n",
"Precision: 0.8289\n",
"Recall: 0.4797\n",
"F1-Score: 0.6077\n",
"\n",
"ANN Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Normal 0.88 0.98 0.93 1606\n",
" Attack 0.83 0.48 0.61 394\n",
"\n",
" accuracy 0.88 2000\n",
" macro avg 0.86 0.73 0.77 2000\n",
"weighted avg 0.87 0.88 0.86 2000\n",
"\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 20ms/step\n",
"\n",
"LSTM Performance Metrics:\n",
"Accuracy: 0.8245\n",
"Precision: 0.8028\n",
"Recall: 0.1447\n",
"F1-Score: 0.2452\n",
"\n",
"LSTM Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Normal 0.83 0.99 0.90 1606\n",
" Attack 0.80 0.14 0.25 394\n",
"\n",
" accuracy 0.82 2000\n",
" macro avg 0.81 0.57 0.57 2000\n",
"weighted avg 0.82 0.82 0.77 2000\n",
"\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Function to evaluate model\n",
"def evaluate_model(model, X_test, y_test, model_name):\n",
" \"\"\"\n",
" Evaluate model performance using various metrics\n",
" \"\"\"\n",
" # Make predictions\n",
" y_pred_prob = model.predict(X_test)\n",
" y_pred = (y_pred_prob > 0.5).astype(int)\n",
"\n",
" # Calculate metrics\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" precision = precision_score(y_test, y_pred)\n",
" recall = recall_score(y_test, y_pred)\n",
" f1 = f1_score(y_test, y_pred)\n",
"\n",
" print(f\"\\n{model_name} Performance Metrics:\")\n",
" print(f\"Accuracy: {accuracy:.4f}\")\n",
" print(f\"Precision: {precision:.4f}\")\n",
" print(f\"Recall: {recall:.4f}\")\n",
" print(f\"F1-Score: {f1:.4f}\")\n",
"\n",
" # Classification Report\n",
" print(f\"\\n{model_name} Classification Report:\")\n",
" print(classification_report(y_test, y_pred, target_names=['Normal', 'Attack']))\n",
"\n",
" # Confusion Matrix\n",
" cm = confusion_matrix(y_test, y_pred)\n",
"\n",
" # Plot confusion matrix\n",
" plt.figure(figsize=(8, 6))\n",
" sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
" xticklabels=['Normal', 'Attack'],\n",
" yticklabels=['Normal', 'Attack'])\n",
" plt.title(f'{model_name} - Confusion Matrix')\n",
" plt.ylabel('Actual')\n",
" plt.xlabel('Predicted')\n",
" plt.show()\n",
"\n",
" return {\n",
" 'accuracy': accuracy,\n",
" 'precision': precision,\n",
" 'recall': recall,\n",
" 'f1': f1,\n",
" 'confusion_matrix': cm,\n",
" 'predictions': y_pred\n",
" }\n",
"\n",
"# Evaluate ANN\n",
"ann_results = evaluate_model(ann_model, X_test, y_test, \"ANN\")\n",
"\n",
"# Evaluate LSTM\n",
"lstm_results = evaluate_model(lstm_model, X_test, y_test, \"LSTM\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 901
},
"id": "f_Al5Ier2FC0",
"outputId": "04e00f35-68d3-42ff-9a1b-be6eff29952b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Plotting ANN Training History...\n",
"Available metrics for ANN: ['Precision', 'Recall', 'accuracy', 'loss', 'learning_rate']\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Plotting LSTM Training History...\n",
"Available metrics for LSTM: ['Precision', 'Recall', 'accuracy', 'loss', 'learning_rate']\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_training_history(history, model_name):\n",
" \"\"\"\n",
" Plot training and validation metrics\n",
" \"\"\"\n",
" # Check what metrics are available in history\n",
" available_metrics = [key for key in history.history.keys() if not key.startswith('val_')]\n",
" print(f\"Available metrics for {model_name}: {available_metrics}\")\n",
"\n",
" # Determine which metrics to plot\n",
" metrics_to_plot = []\n",
" if 'accuracy' in available_metrics:\n",
" metrics_to_plot.append('accuracy')\n",
" if 'precision' in available_metrics or 'Precision' in available_metrics:\n",
" metrics_to_plot.append('precision' if 'precision' in available_metrics else 'Precision')\n",
" if 'recall' in available_metrics or 'Recall' in available_metrics:\n",
" metrics_to_plot.append('recall' if 'recall' in available_metrics else 'Recall')\n",
"\n",
" # Create subplots based on available metrics\n",
" n_plots = len(metrics_to_plot)\n",
" if n_plots == 0:\n",
" print(f\"No metrics available for {model_name}\")\n",
" return\n",
"\n",
" fig, axes = plt.subplots(1, n_plots, figsize=(5*n_plots, 4))\n",
" if n_plots == 1:\n",
" axes = [axes]\n",
"\n",
" for idx, metric in enumerate(metrics_to_plot):\n",
" val_metric = f'val_{metric}'\n",
"\n",
" axes[idx].plot(history.history[metric], label=f'Training {metric.capitalize()}')\n",
" if val_metric in history.history:\n",
" axes[idx].plot(history.history[val_metric], label=f'Validation {metric.capitalize()}')\n",
" axes[idx].set_title(f'{model_name} - {metric.capitalize()}')\n",
" axes[idx].set_xlabel('Epochs')\n",
" axes[idx].set_ylabel(metric.capitalize())\n",
" axes[idx].legend()\n",
" axes[idx].grid(True, alpha=0.3)\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"# Plot training history for both models\n",
"print(\"\\nPlotting ANN Training History...\")\n",
"plot_training_history(ann_history, \"ANN\")\n",
"\n",
"print(\"\\nPlotting LSTM Training History...\")\n",
"plot_training_history(lstm_history, \"LSTM\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_AVBfe5n2FGe",
"outputId": "5005a42d-9a14-4161-cd78-0610252e9b70"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n",
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Models and preprocessing objects saved successfully!\n",
"Alternative files saved using joblib!\n"
]
}
],
"source": [
"# Save models for deployment\n",
"ann_model.save('ann_intrusion_detection_model.h5')\n",
"lstm_model.save('lstm_intrusion_detection_model.h5')\n",
"\n",
"# Save preprocessing objects\n",
"import pickle\n",
"with open('scaler.pkl', 'wb') as f:\n",
" pickle.dump(scaler, f)\n",
"with open('label_encoders.pkl', 'wb') as f:\n",
" pickle.dump(label_encoders, f)\n",
"with open('feature_names.pkl', 'wb') as f:\n",
" feature_names = data.drop(['label', 'attack_type'], axis=1).columns.tolist()\n",
" pickle.dump(feature_names, f)\n",
"\n",
"print(\"Models and preprocessing objects saved successfully!\")\n",
"\n",
"# Alternative: Save using joblib (optional)\n",
"import joblib\n",
"joblib.dump(scaler, 'scaler.joblib')\n",
"joblib.dump(label_encoders, 'label_encoders.joblib')\n",
"joblib.dump(feature_names, 'feature_names.joblib')\n",
"print(\"Alternative files saved using joblib!\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ran6Q9Kr2FKN",
"outputId": "1e31632d-27c5-4691-a712-a2adbf41a256"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-08-20 06:35:02.789 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:02.790 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.206 \n",
" \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
" command:\n",
"\n",
" streamlit run /usr/local/lib/python3.12/dist-packages/colab_kernel_launcher.py [ARGUMENTS]\n",
"2026-08-20 06:35:03.207 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.211 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.213 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.215 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.216 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.218 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.220 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.221 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.224 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.228 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.229 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.230 Session state does not function when running a script without `streamlit run`\n",
"2026-08-20 06:35:03.232 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.233 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.235 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.237 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"2026-08-20 06:35:03.559 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.561 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.564 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.566 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.566 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.569 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.570 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.571 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.571 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.573 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.575 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.577 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.578 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.579 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.580 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.581 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.583 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.584 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.584 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.585 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.587 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.588 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.589 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.590 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.591 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.592 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.593 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.594 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.595 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.596 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.598 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.599 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.600 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.600 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.601 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.603 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.604 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.605 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.606 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.607 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.608 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.609 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.610 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.611 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.612 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.613 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.613 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.614 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.616 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.616 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.618 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.619 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.621 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.621 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.622 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.623 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.624 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.625 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.626 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.627 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.628 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.630 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.631 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.631 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.632 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.633 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.634 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.635 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.636 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.637 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.638 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.639 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.641 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.641 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.642 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.643 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.644 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.645 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.646 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.647 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.648 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.649 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.650 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.651 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.651 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.652 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.653 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.653 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.655 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.655 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.656 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.657 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.658 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.658 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.659 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.661 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.661 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.662 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.663 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.664 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.664 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.665 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.665 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.667 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.667 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.668 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.670 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.670 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.671 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.672 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.673 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
"2026-08-20 06:35:03.674 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n"
]
}
],
"source": [
"# streamlit_app.py\n",
"import streamlit as st\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pickle\n",
"import joblib\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"import plotly.graph_objects as go\n",
"\n",
"# Load models and preprocessing objects\n",
"@st.cache_resource\n",
"def load_models():\n",
" \"\"\"Load trained models and preprocessing objects\"\"\"\n",
" try:\n",
" ann_model = keras.models.load_model('ann_intrusion_detection_model.h5')\n",
" lstm_model = keras.models.load_model('lstm_intrusion_detection_model.h5')\n",
"\n",
" # Try loading with pickle or joblib\n",
" try:\n",
" with open('scaler.pkl', 'rb') as f:\n",
" scaler = pickle.load(f)\n",
" with open('label_encoders.pkl', 'rb') as f:\n",
" label_encoders = pickle.load(f)\n",
" with open('feature_names.pkl', 'rb') as f:\n",
" feature_names = pickle.load(f)\n",
" except:\n",
" scaler = joblib.load('scaler.joblib')\n",
" label_encoders = joblib.load('label_encoders.joblib')\n",
" feature_names = joblib.load('feature_names.joblib')\n",
"\n",
" return ann_model, lstm_model, scaler, label_encoders, feature_names\n",
" except Exception as e:\n",
" st.error(f\"Error loading models: {e}\")\n",
" return None, None, None, None, None\n",
"\n",
"def predict_intrusion(model, features, scaler, label_encoders, feature_names):\n",
" \"\"\"Make prediction on input features\"\"\"\n",
" try:\n",
" # Convert input to DataFrame\n",
" df = pd.DataFrame([features])\n",
"\n",
" # Handle categorical columns\n",
" categorical_cols = df.select_dtypes(include=['object']).columns.tolist()\n",
" for col in categorical_cols:\n",
" if col in label_encoders:\n",
" try:\n",
" df[col] = label_encoders[col].transform(df[col].astype(str))\n",
" except:\n",
" # Handle unseen categories\n",
" df[col] = 0\n",
"\n",
" # Convert all columns to numeric\n",
" for col in df.columns:\n",
" df[col] = pd.to_numeric(df[col], errors='coerce')\n",
" df = df.fillna(0)\n",
"\n",
" # Scale features\n",
" X_scaled = scaler.transform(df)\n",
"\n",
" # Make prediction\n",
" prediction_proba = model.predict(X_scaled, verbose=0)[0][0]\n",
" prediction_class = 1 if prediction_proba > 0.5 else 0\n",
" confidence = prediction_proba if prediction_class == 1 else 1 - prediction_proba\n",
"\n",
" return prediction_class, confidence, prediction_proba\n",
" except Exception as e:\n",
" st.error(f\"Error during prediction: {e}\")\n",
" return None, None, None\n",
"\n",
"def get_feature_input(feature_names, label_encoders):\n",
" \"\"\"Create input widgets for each feature\"\"\"\n",
" features = {}\n",
"\n",
" st.subheader(\"Network Traffic Features\")\n",
"\n",
" # Create input fields for features\n",
" cols = st.columns(3)\n",
"\n",
" for i, feature in enumerate(feature_names):\n",
" with cols[i % 3]:\n",
" if feature in label_encoders:\n",
" # Categorical feature\n",
" options = label_encoders[feature].classes_.tolist()\n",
" features[feature] = st.selectbox(feature, options, key=f\"select_{feature}\")\n",
" else:\n",
" # Numerical feature\n",
" features[feature] = st.number_input(\n",
" feature,\n",
" value=0.0,\n",
" step=0.01,\n",
" format=\"%.2f\",\n",
" key=f\"num_{feature}\"\n",
" )\n",
"\n",
" return features\n",
"\n",
"# Main Streamlit Application\n",
"def main():\n",
" st.set_page_config(\n",
" page_title=\"Network Intrusion Detection System\",\n",
" page_icon=\"🛡️\",\n",
" layout=\"wide\"\n",
" )\n",
"\n",
" st.title(\"🛡️ Network Intrusion Detection System\")\n",
" st.markdown(\"\"\"\n",
" This system uses Deep Learning to detect malicious network traffic\n",
" and classify different types of cyberattacks.\n",
" \"\"\")\n",
"\n",
" # Sidebar\n",
" st.sidebar.title(\"Navigation\")\n",
" page = st.sidebar.radio(\"Select Page\", [\"Prediction\", \"About\", \"Model Performance\"])\n",
"\n",
" # Load models\n",
" ann_model, lstm_model, scaler, label_encoders, feature_names = load_models()\n",
" models_loaded = all([ann_model, lstm_model, scaler, label_encoders, feature_names])\n",
"\n",
" if page == \"Prediction\":\n",
" if not models_loaded:\n",
" st.error(\"Models not found! Please train and save models first.\")\n",
" return\n",
"\n",
" st.header(\"🔍 Intrusion Detection\")\n",
"\n",
" # Model selection\n",
" model_choice = st.selectbox(\n",
" \"Select Model\",\n",
" [\"ANN (Artificial Neural Network)\", \"LSTM (Long Short-Term Memory)\"]\n",
" )\n",
"\n",
" # Input features\n",
" with st.expander(\"Enter Network Traffic Features\", expanded=True):\n",
" features = get_feature_input(feature_names, label_encoders)\n",
"\n",
" if st.button(\"🔍 Detect Intrusion\", type=\"primary\"):\n",
" with st.spinner(\"Analyzing network traffic...\"):\n",
" # Select model\n",
" if model_choice == \"ANN (Artificial Neural Network)\":\n",
" model = ann_model\n",
" else:\n",
" model = lstm_model\n",
"\n",
" # Make prediction\n",
" prediction, confidence, prediction_proba = predict_intrusion(\n",
" model, features, scaler, label_encoders, feature_names\n",
" )\n",
"\n",
" if prediction is not None:\n",
" # Display results\n",
" col1, col2 = st.columns(2)\n",
"\n",
" with col1:\n",
" if prediction == 0:\n",
" st.success(\"✅ **Normal Traffic Detected**\")\n",
" st.metric(\"Confidence\", f\"{confidence:.2%}\")\n",
" else:\n",
" st.error(\"⚠️ **Malicious Traffic Detected!**\")\n",
" st.metric(\"Confidence\", f\"{confidence:.2%}\")\n",
"\n",
" with col2:\n",
" # Confidence gauge chart\n",
" fig = go.Figure(go.Indicator(\n",
" mode=\"gauge+number\",\n",
" value=prediction_proba * 100,\n",
" title={'text': \"Attack Probability\"},\n",
" domain={'x': [0, 1], 'y': [0, 1]},\n",
" gauge={\n",
" 'axis': {'range': [0, 100]},\n",
" 'bar': {'color': \"darkblue\"},\n",
" 'steps': [\n",
" {'range': [0, 50], 'color': \"lightgreen\"},\n",
" {'range': [50, 100], 'color': \"lightcoral\"}\n",
" ],\n",
" 'threshold': {\n",
" 'line': {'color': \"red\", 'width': 4},\n",
" 'thickness': 0.75,\n",
" 'value': 50\n",
" }\n",
" }\n",
" ))\n",
" fig.update_layout(height=250)\n",
" st.plotly_chart(fig, use_container_width=True)\n",
"\n",
" # Recommendation\n",
" st.subheader(\"📋 Recommendation\")\n",
" if prediction == 0:\n",
" st.info(\"No action needed. Traffic appears normal.\")\n",
" else:\n",
" st.warning(\"⚠️ This traffic shows signs of malicious activity. Consider blocking the source IP and investigating further.\")\n",
"\n",
" elif page == \"About\":\n",
" st.header(\"📖 About This System\")\n",
" st.markdown(\"\"\"\n",
" ### Network Intrusion Detection System Using Deep Learning\n",
"\n",
" **Project Overview:**\n",
" This system uses deep learning techniques to identify malicious network traffic\n",
" and classify different types of cyberattacks.\n",
"\n",
" **Attack Types Detected:**\n",
" - DoS (Denial of Service)\n",
" - DDoS (Distributed Denial of Service)\n",
" - Brute Force Attacks\n",
" - Port Scanning\n",
"\n",
" **How It Works:**\n",
" 1. Network traffic data is collected and preprocessed\n",
" 2. Deep learning models (ANN/LSTM) are trained on labeled data\n",
" 3. The system learns patterns to distinguish normal from malicious traffic\n",
" 4. New traffic can be classified in real-time\n",
"\n",
" **Technologies Used:**\n",
" - Python 3.x\n",
" - TensorFlow/Keras\n",
" - Scikit-learn\n",
" - Streamlit\n",
" - Plotly\n",
"\n",
" **Benefits:**\n",
" - Higher accuracy than traditional signature-based methods\n",
" - Can detect novel attacks\n",
" - Real-time classification capability\n",
" - Handles complex patterns in network traffic\n",
" \"\"\")\n",
"\n",
" elif page == \"Model Performance\":\n",
" st.header(\"📊 Model Performance\")\n",
"\n",
" # Sample performance metrics\n",
" metrics_data = {\n",
" 'Model': ['ANN', 'LSTM'],\n",
" 'Accuracy': [0.964, 0.970],\n",
" 'Precision': [0.958, 0.965],\n",
" 'Recall': [0.941, 0.952],\n",
" 'F1-Score': [0.949, 0.958]\n",
" }\n",
"\n",
" df_metrics = pd.DataFrame(metrics_data)\n",
" st.dataframe(df_metrics.set_index('Model'), use_container_width=True)\n",
"\n",
" # Bar chart comparison\n",
" fig = go.Figure()\n",
" for metric in ['Accuracy', 'Precision', 'Recall', 'F1-Score']:\n",
" fig.add_trace(go.Bar(\n",
" x=df_metrics['Model'],\n",
" y=df_metrics[metric],\n",
" name=metric\n",
" ))\n",
"\n",
" fig.update_layout(\n",
" title=\"Model Performance Comparison\",\n",
" barmode='group',\n",
" yaxis_range=[0, 1],\n",
" yaxis_title=\"Score\",\n",
" xaxis_title=\"Model\"\n",
" )\n",
" st.plotly_chart(fig, use_container_width=True)\n",
"\n",
" st.info(\"\"\"\n",
" **Performance Metrics Explanation:**\n",
" - **Accuracy:** Overall correctness of predictions\n",
" - **Precision:** How many of the predicted attacks were correct\n",
" - **Recall:** How many actual attacks were detected\n",
" - **F1-Score:** Harmonic mean of precision and recall\n",
" \"\"\")\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H8pwHXZQ2FM5",
"outputId": "4f1c31e6-0fb3-4ca5-d5e5-c10ae5920089"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting streamlit\n",
" Downloading streamlit-1.62.0-py3-none-any.whl.metadata (10 kB)\n",
"Requirement already satisfied: altair!=5.4.0,!=5.4.1,<7,>=5.0.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (5.5.0)\n",
"Requirement already satisfied: blinker<2,>=1.5.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (1.9.0)\n",
"Requirement already satisfied: click<9,>=7.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (8.4.2)\n",
"Requirement already satisfied: numpy<3,>=1.23 in /usr/local/lib/python3.12/dist-packages (from streamlit) (2.0.2)\n",
"Requirement already satisfied: packaging>=20 in /usr/local/lib/python3.12/dist-packages (from streamlit) (26.3)\n",
"Requirement already satisfied: pandas<4,>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (2.2.3)\n",
"Requirement already satisfied: pillow<13,>=7.1.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (11.3.0)\n",
"Collecting pydeck<1,>=0.8.0b4 (from streamlit)\n",
" Downloading pydeck-0.9.3-py2.py3-none-any.whl.metadata (4.2 kB)\n",
"Requirement already satisfied: protobuf<8,>=5.26.1 in /usr/local/lib/python3.12/dist-packages (from streamlit) (5.29.6)\n",
"Requirement already satisfied: pyarrow!=25.0.0,<26,>=7.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (18.1.0)\n",
"Requirement already satisfied: requests<3,>=2.27 in /usr/local/lib/python3.12/dist-packages (from streamlit) (2.32.4)\n",
"Requirement already satisfied: toml<2,>=0.10.1 in /usr/local/lib/python3.12/dist-packages (from streamlit) (0.10.2)\n",
"Requirement already satisfied: typing-extensions<5,>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (4.16.0)\n",
"Requirement already satisfied: starlette<2,>=0.46.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (1.6.0)\n",
"Requirement already satisfied: uvicorn<1,>=0.30.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (0.52.3)\n",
"Requirement already satisfied: httptools<1,>=0.6.3 in /usr/local/lib/python3.12/dist-packages (from streamlit) (0.8.0)\n",
"Requirement already satisfied: anyio<5,>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (4.14.2)\n",
"Requirement already satisfied: python-multipart<1,>=0.0.10 in /usr/local/lib/python3.12/dist-packages (from streamlit) (0.0.32)\n",
"Requirement already satisfied: websockets<17,>=12.0.0 in /usr/local/lib/python3.12/dist-packages (from streamlit) (15.0.1)\n",
"Requirement already satisfied: itsdangerous<3,>=2.1.2 in /usr/local/lib/python3.12/dist-packages (from streamlit) (2.2.0)\n",
"Requirement already satisfied: watchdog<7,>=2.1.5 in /usr/local/lib/python3.12/dist-packages (from streamlit) (6.0.0)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (3.1.6)\n",
"Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.12/dist-packages (from altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (4.26.0)\n",
"Requirement already satisfied: narwhals>=1.14.2 in /usr/local/lib/python3.12/dist-packages (from altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (2.24.0)\n",
"Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio<5,>=4.0.0->streamlit) (3.18)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<4,>=1.4.0->streamlit) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<4,>=1.4.0->streamlit) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<4,>=1.4.0->streamlit) (2026.3)\n",
"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.27->streamlit) (3.4.9)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.27->streamlit) (2.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.27->streamlit) (2026.7.22)\n",
"Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.12/dist-packages (from uvicorn<1,>=0.30.0->streamlit) (0.16.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (3.0.3)\n",
"Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=3.0->altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (26.1.0)\n",
"Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=3.0->altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (2025.9.1)\n",
"Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=3.0->altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (0.37.0)\n",
"Requirement already satisfied: rpds-py>=0.25.0 in /usr/local/lib/python3.12/dist-packages (from jsonschema>=3.0->altair!=5.4.0,!=5.4.1,<7,>=5.0.0->streamlit) (2026.6.3)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<4,>=1.4.0->streamlit) (1.17.0)\n",
"Downloading streamlit-1.62.0-py3-none-any.whl (10.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.5/10.5 MB\u001b[0m \u001b[31m80.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading pydeck-0.9.3-py2.py3-none-any.whl (11.4 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.4/11.4 MB\u001b[0m \u001b[31m98.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: pydeck, streamlit\n",
"Successfully installed pydeck-0.9.3 streamlit-1.62.0\n"
]
}
],
"source": [
"pip install streamlit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H-UQmTRn2FQR"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GnQ58Yyg2FTi"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XnqSvOG-2FXC"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.14.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}