对卫星系统遥测遥控的间歇干扰及学习驱动检测策略
Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy
摘要：
面向第六代网络（6G），卫星通信系统，特别是基于低地球轨道（LEO）网络的卫星通信系统，由于其独特和全面的能力而变得有前途。这些优势伴随着各种挑战，例如安全漏洞、混杂系统的管理和高移动性。在本文中，首先，在物理层的安全缺陷是解决一个概念框架，考虑到卫星系统的网络物理性质，突出潜在的攻击。其次，提出了一种学习驱动的检测方案，并设计了轻量级卷积神经网络（CNN）。所设计的CNN架构的性能进行了比较与流行的机器学习算法，支持向量机（SVM）。实验结果表明，该方案能够检测出针对卫星系统的缺陷攻击。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
