SCER Spoofing Attacks on the Galileo Open Service and Machine Learning Techniques for End-User Protection

Abstract

Spoofing attacks pose a clear cybersecurity risk for all systems relying on Global Navigation Satellite Systems (GNSS) for time synchronization or positioning. Secure Code Estimation and Replay (SCER) spoofing attacks are the most challenging type of spoofing attacks, as these may be problematic even for future GNSS protection systems, like Navigation Message Authentication (NMA) or Spreading Code Authentication (SCA). This is one of the reasons that make the development of complementary protection techniques, like the one proposed in this work, necessary. In the first part of the paper, the spoofing SCER attacks are analyzed in detail for GPS and, particularly, for Galileo. The role of the Galileo Pseudorandom Noise (PRN) intra-satellite non-orthogonality distortion term in hindering the attacks is discussed and a detailed comparison between GPS and Galileo expected quality curves for the SCER attack is provided. A complementary detection method for end-user receivers (assuming NMA is used) against SCER attacks is proposed, based on the application of machine learning and a proposed set of features extracted from the receiver search space, assuming the attacker was not able to null the satellite signal.