Create ai_trainer.py

Author: ProjectZeroDays

src/ai/ai_training/ai_trainer.py

@@ -0,0 +1,86 @@
+import logging
+import tensorflow as tf
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+
+logging.basicConfig(level=logging.ERROR)
+
+def train_model(training_data, model_path, config):
+    logging.info("Starting AI model training")
+    if not training_data:
+        logging.error("Training data is empty.")
+        return
+    learning_rate = config['ai']['learning_rate']
+    # Load data and preprocess
+    X, y = preprocess_data(training_data)
+    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Define model architecture
+    model = create_model(learning_rate, X_train.shape[1], config)
+
+    # Train the model
+    model.fit(X_train, y_train, epochs=100, validation_data=(X_val, y_val))
+
+    # Evaluate the model
+    y_pred = model.predict(X_val)
+    y_pred = (y_pred > 0.5).astype(int)
+    accuracy = accuracy_score(y_val, y_pred)
+    precision = precision_score(y_val, y_pred, average='weighted', zero_division=0)
+    recall = recall_score(y_val, y_pred, average='weighted', zero_division=0)
+    f1 = f1_score(y_val, y_pred, average='weighted', zero_division=0)
+    logging.info(f"Model Evaluation: Accuracy={accuracy:.4f}, Precision={precision:.4f}, Recall={recall:.4f}, F1-Score={f1:.4f}")
+
+    # Save the model
+    model.save(model_path)
+    logging.info("AI model training completed")
+
+def preprocess_data(training_data):
+    logging.info("Preprocessing training data")
+    # Example data: [target_ip, target_port, exploit_type, outcome]
+    X = np.array([[item[0], item[1], item[2]] for item in training_data])
+    y = np.array([item[3] for item in training_data])
+
+    # One-hot encode exploit_type
+    encoder = OneHotEncoder(handle_unknown='ignore')
+    X_encoded = encoder.fit_transform(X[:, [2]]).toarray()
+
+    # Scale numerical features
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X[:, :2])
+
+    # Combine encoded and scaled features
+    X_processed = np.concatenate((X_scaled, X_encoded), axis=1)
+    return X_processed, y
+
+def create_model(learning_rate, input_shape, config):
+    logging.info("Creating AI model")
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Dense(config['ai']['dense_layer_1'], activation='relu', input_shape=(input_shape,)),
+        tf.keras.layers.Dense(config['ai']['dense_layer_2'], activation='relu'),
+        tf.keras.layers.Dense(1, activation='sigmoid')
+    ])
+    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
+    model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
+    return model
+
+def integrate_with_new_components(new_component_data, config):
+    logging.info("Integrating with new components")
+    # Placeholder for integration logic with new components
+    integrated_data = {
+        "new_component_training_data": new_component_data.get("training_data", []),
+        "new_component_model_config": new_component_data.get("model_config", {})
+    }
+    return integrated_data
+
+def ensure_compatibility(existing_data, new_component_data, config):
+    logging.info("Ensuring compatibility with existing AI training logic")
+    # Placeholder for compatibility logic
+    compatible_data = {
+        "existing_training_data": existing_data.get("training_data", []),
+        "existing_model_config": existing_data.get("model_config", {}),
+        "new_component_training_data": new_component_data.get("training_data", []),
+        "new_component_model_config": new_component_data.get("model_config", {})
+    }
+    return compatible_data