Multiple Spoofer Detection for Mobile GNSS Receivers Using Statistical Tests

We consider Global Navigation Satellite Systems (GNSS) spoofing attacks and devise a countermeasure appropriate for mobile GNSS receivers. Our approach is to design detectors that, operating after the signal acquisition, enable the victim receiver to determine with high probability whether it is under a spoofing attack or not. Namely, the binary hypothesis is that either the GNSS receiver tracks legitimate satellite signals,
$\mathcal {H}_{0}$
, or spoofed signals,
$\mathcal {H}_{1}$
. We assume that there exists an unknown number of multiple spoofers in the environment and the attack strategy (which legitimate signals are spoofed by which spoofers) is not known to the receiver. Based on these assumptions, we propose an algorithm that identifies the number of spoofers and clusters the spoofing data by using Bayesian information criterion (BIC) rule. Depending on the estimated and clustered data we propose a detector, called as generalized likelihood ratio (GLRT)-like detector. We compare the performance of the GLRT-like detector with a genie-aided detector in which the attack strategy and the number of spoofers is known by the receiver. In addition to this, we extend the GLRT-like detector for the case where the noise variance is also unknown and present the performance results.