Karunathilake K. K. H.2026-02-102025-12https://rda.sliit.lk/handle/123456789/4586The 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.enAI- DrivenIntrusion Detection SystemDefend AgainstSatellite HijackingThesis