G.O.D Framework

Introduction

The ai_security_anomaly_detector.py module is a critical component within the G.O.D Framework that focuses on proactive identification and mitigation of security threats, such as unauthorized access, malicious attacks, and data breaches. By leveraging machine learning techniques, it continuously monitors system behavior and flags anomalies based on adaptive threat detection models.

Purpose

The primary objectives of this module include:

• Real-time monitoring of security-sensitive activities to detect anomalies.
• Flagging suspicious activities and generating automated alerts for system administrators.
• Adapting to new threats using machine learning-based behavior analysis.
• Protecting sensitive data and infrastructure against internal and external threats.

Key Features

• Behavioral Analysis: Monitors user and system behavior to detect deviations from normal patterns.
• Anomaly Detection Models: Employs ML techniques like Isolation Forests and Autoencoders to detect anomalies.
• Event Correlation: Links seemingly unrelated events to identify coordinated attacks or vulnerabilities.
• Actionable Alerts: Triggers alerts with detailed findings for administrators to act on.
• Threat Adaptation: Updates models dynamically to stay resilient against evolving attack patterns.

Logic and Implementation

The anomaly detection logic combines predictive models, rule-based filters, and statistical methods to identify and mitigate potential security breaches. Here’s an example of implementing anomaly detection using the scikit-learn library:

import numpy as np
from sklearn.ensemble import IsolationForest
import logging

class SecurityAnomalyDetector:
    """
    Detects security anomalies using machine learning models and behavioral analysis.
    """
    def __init__(self):
        try:
            self.model = IsolationForest(contamination=0.05, random_state=42)
            logging.info("Security Anomaly Detector initialized successfully.")
        except Exception as e:
            logging.error(f"Failed to initialize the Security Anomaly Detector: {e}")

    def train(self, data):
        """
        Trains the anomaly detection model on historical data.

        Args:
            data (numpy.ndarray): Array of historical system metrics or user activity.

        Returns:
            str: Training status.
        """
        try:
            self.model.fit(data)
            return "Training completed successfully."
        except Exception as e:
            logging.error(f"Error during training: {e}")
            return str(e)

    def detect_anomalies(self, data):
        """
        Detects anomalies in incoming data.

        Args:
            data (numpy.ndarray): Array of new system metrics or user activity.

        Returns:
            list: A list of anomaly detection results (-1 indicates anomalies, 1 indicates normal).
        """
        try:
            predictions = self.model.predict(data)
            logging.info("Anomaly detection completed.")
            return predictions
        except Exception as e:
            logging.error(f"Error during anomaly detection: {e}")
            return []

# Example usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    # Example system metrics (replace with real data)
    historical_data = np.random.rand(100, 5)
    new_data = np.random.rand(10, 5)

    detector = SecurityAnomalyDetector()
    print(detector.train(historical_data))

    anomalies = detector.detect_anomalies(new_data)
    print("Anomaly Results:", anomalies)
        

Dependencies

• scikit-learn: Core for implementing anomaly detection models such as Isolation Forests.
• numpy: Handles numerical computations for input data arrays.
• logging: Logs all anomaly detection operations for debugging and forensic analysis.

Integration with G.O.D Framework

• ai_audit_logger.py: Logs anomalous events into the audit trail for further investigation.
• ai_alerting.py: Sends real-time alerts to administrators upon detections of critical anomalies.
• ai_secure_data_handler.py: Works in conjunction to safeguard sensitive data from anomalous behaviors.

Future Enhancements

Potential areas for improvement include:

• Integrating deep learning-based anomaly detection for complex and high-dimensional datasets.
• Building a dashboard for visualizing anomaly patterns and trends over time.
• Expanding the detection engine to include network-based anomalies in addition to behavioral analysis.
• Automating anomaly remediation actions to minimize response times.