Adaptive Detection of On-Orbit Jamming for Securing GEO Satellite Links

TL;DR

This paper presents a machine learning-based method combining PCA and adaptive thresholding to detect on-orbit jamming attacks on GEO satellite links, improving robustness and accuracy in dynamic threat scenarios.

Contribution

It introduces a novel adaptive detection framework that integrates satellite orbital dynamics with machine learning techniques for enhanced jamming detection.

Findings

PCA improves stationary jamming detection accuracy.

Adaptive thresholding effectively detects dynamic jamming events.

Simulation results confirm high detection performance.

Abstract

This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground station, with the ability to switch between stationary and time-variant jamming modes. We propose a machine learning-based detection approach, employing the random forest algorithm with principal component analysis (PCA) to enhance detection accuracy in the stationary model. At the same time, an adaptive threshold-based technique is implemented for the time-variant model to detect dynamic jamming events effectively. Our methodology emphasizes the need for the use of orbital dynamics in integrating physical constraints from satellite dynamics to improve model robustness and detection accuracy. Simulation results highlight the effectiveness of PCA in enhancing the performance of the stationary model, while the adaptive thresholding method achieves high accuracy in detecting jamming in the time-variant scenario. This approach provides a robust solution for mitigating the evolving threats to satellite communication in GEO environments.