An AI-Driven Intrusion Detection System to Defend Against Satellite Hijacking

Abstract

The increasing reliance of the world on satellite systems has made them prime targets for cyber threats, with satellite orbital manipulation, a form of satellite hijacking, posing a critical national security risk due to its potential for disrupting essential infrastructure. To address this threat, this research proposes a novel Artificial Intelligence (AI)-based anomaly detection system tailored for identifying suspicious orbital maneuvers. The study employs Machine Learning (ML) models to analyze a custom dataset derived from the public European Space Agency Anomaly Detection Benchmark (ESA-ADB). This dataset was rigorously pre-filtered to include only anomalies occurring within a ±48.00 hours window of a telecommand execution, thereby creating a naturally balanced, command-linked dataset to proxy for the kinematic footprint of a cyberattack. Findings established that temporal pattern recognition is paramount for detecting these attacks. LSTM networks emerged as the most promising model, leveraging their ability to learn sequential dependencies to achieve a high recall rate of 95.64% with a corresponding precision of 90.88%. Furthermore, a novel physics validation gate, grounded in orbital mechanics, was incorporated as a final, non-negotiable security layer. This component is vital, as it confirms that detected anomalies are physically non-nominal deviations, transforming raw statistical alerts into high-confidence cybersecurity indicators and dramatically boosting the overall trustworthiness and suitability of the system for operational deployment.