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

• URL: http://arxiv.org/abs/2107.06181v1
• Date: Sat, 10 Jul 2021 17:04:22 GMT
• Title: Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy
• Authors: Selen Gecgel and Gunes Karabulut Kurt
• Abstract summary: A security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems. A learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed. The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.
• Score: 1.4620086904601468
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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