Hypothesis testing methods to detect spoofing attacks: a test against the TEXBAT datasets

Abstract

The hazardous effects of spoofing attacks on the global navigation satellite system (GNSS) receiver are well known. Technologies and algorithms to increase the awareness of GNSS receivers against such attacks become more important and necessary. We present the validation of two statistical spoofing detection methods, namely the Chi-square goodness of fit (GoF) test and the Sign test applied to pairwise correlator differences, for each satellite tracked by the receiver. The test bench for the algorithms, both implemented in a software receiver, is the public database produced by the University of Texas at Austin, which reproduces various representative cases of spoofing attacks (the so-called TEXBAT). The algorithms show a very promising capability of detecting the attack, in particular when an aggregate decision is taken based on a joint detection upon all the tracked satellites. Furthermore, the GoF test appears also reliable in dynamic conditions and in case of a huge power advantage of the spoofing signal. The response of the receiver to the attacks confirms the spoofing signal represents an “extraneous agent” which, before taking control of the receiver, can be recognized by properly combined strategies of signal quality monitoring.