Statistical Characterization of Snapshot OSNMA Spoofing Detection.

Spoofing detection in Global Navigation Satellite Systems (GNSS) is gradually becoming need of the hour due to significant increase in sophisticated spoofing attacks that compromise the signal integrity and security. To withstand against these attacks, Galileo is providing the Open Service Navigation Message Authentication (OSNMA) in its E1-B signal component, comprises of a cryptographic protocol that conveys unpredictable data symbols to the user to verify the content of the I/NAV message. In this context, the following paper proposes a reliable spoofer detector by employing the snapshots of received unpredictable symbols and compares them with the authentic ones. The problem is formulated as a Binary Symmetric Channel (BSC), where the feasibility is determined by the probabilities of error at the spoofer's and the user's sides. However, due to the presence of signal impairments, the spoofing detector faces the hypothesis inversion problem (i.e. chooses the wrong hypothesis under certain conditions). The primary focus of this article is to avoid the hypothesis inversion problem by optimizing the statistical characterization of snapshot OSNMA detector and enhance the detection performance by designing appropriate test statistics conditions. Simulation results reveal that utilizing multiple test conditions solves the problem and strengthens the detection performance to a great extent.