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This project addresses cybersecurity in aviation by developing a machine learning-enhanced intrusion detection and prevention system (IDPS) for aircraft networks. Combining YARA-based signature detection with behavior-based (ML) anomaly detection, the system mitigates cyber threats in real-time, protecting aircraft from sophisticated attacks.

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aishwaryagm1999/Aircraft-Network-Security-using-YARA-Rules-and-Machine-Learning-for-Threat-Detection-and-Prevention

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Aircraft-Network-Security-using-YARA-Rules-and-Machine-Learning-for-Threat-Detection-and-Prevention

Overview

This project introduces an Intrusion Detection and Prevention System (IDPS) specifically designed for the unique cybersecurity requirements of aircraft networks. Leveraging YARA for signature-based detection alongside machine learning for anomaly detection, this system enhances security by identifying and mitigating cyber threats in real time. The IDPS integrates both signature and behavior-based patterns, providing robust protection for aircraft networks from cyber threats like malware, DDoS attacks, and unauthorized access attempts.

Features

  • Signature-Based Detection: Utilizes YARA rules for malware signature detection.
  • Machine Learning Anomaly Detection: Employs random forest classifiers and feature hashing for behavior-based threat identification.
  • Real-Time Alerts: Sends instant alerts to pilots and ground control upon detecting threats.
  • Incident Response: Implements automated responses, including blocking IPs and restricting access to compromised zones.

About

This project addresses cybersecurity in aviation by developing a machine learning-enhanced intrusion detection and prevention system (IDPS) for aircraft networks. Combining YARA-based signature detection with behavior-based (ML) anomaly detection, the system mitigates cyber threats in real-time, protecting aircraft from sophisticated attacks.

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