  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.
