Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy

TL;DR

This paper addresses security vulnerabilities in satellite communication systems by proposing a learning-driven detection scheme using a lightweight CNN, which effectively detects cyber-physical attacks in 6G satellite networks.

Contribution

It introduces a novel security framework for satellite systems and develops a lightweight CNN-based detection method, outperforming traditional SVM approaches.

Findings

CNN outperforms SVM in attack detection accuracy

Proposed scheme effectively identifies cyber-physical attacks

Framework enhances security in 6G satellite communications

Abstract

Towards sixth-generation networks (6G), satellite communication systems, especially based on Low Earth Orbit (LEO) networks, become promising due to their unique and comprehensive capabilities. These advantages are accompanied by a variety of challenges such as security vulnerabilities, management of hybrid systems, and high mobility. In this paper, firstly, a security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems, highlighting the potential attacks. Secondly, a learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed. The performance of the designed CNN architecture is compared with a prevalent machine learning algorithm, support vector machine (SVM). The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.