Addressing Spoofing and Unauthorized Access: DL-Based Satellite Physical-Layer Authentication

The rapid proliferation of low Earth orbit (LEO) satellite constellations is gradually reshaping the global communication landscape. In the future, satellite communications will become an integral part of integrated ground-air-space networks, but this also introduces significant security challenges, such as spoofing and unauthorized access. Radio Frequency Fingerprinting (RFF), leveraging hardware-induced signal characteristics, offers a promising solution for physical-layer authentication. However, the highly dynamic LEO environment-characterized by rapid relative motion, Doppler effects, and volatile propagation conditions-makes extracting robust RF fingerprints complex. In this study, we propose a novel RFF authentication scheme tailored for LEO satellites within the Iridium constellation. By leveraging deep learning techniques, we extract and classify RF fingerprints directly from raw IQ samples, avoiding extensive preprocessing or demodulation. Our system integrates a Doppler-based signal-matching method to ensure accurate labeling and employs a Multi-Scale Inline Star Convolutional Network (MSLS) to enhance feature extraction. Experimental results demonstrate that our method achieves an authentication accuracy of 88% using significantly fewer training samples compared to existing approaches, making it more efficient and practical for real-world deployment. This work paves the way for robust, scalable, low-cost authentication solutions for satellite communication networks.