Improving GNSS Spoofing Awareness in Smartphones via Statistical Processing of Raw Measurements

Due to the low received power of Global Navigation Satellite Signals (GNSS), the performance of GNSS receivers can be disrupted by anthropogenic radio frequency interferences, with intentional jamming and spoofing activities being among the most critical threats. It is reported in the literature that modern, GNSS-equipped Android smartphones are generally resistant to simplistic spoofing, and many recent contributions support such a biased belief. In this paper, we present the results of a test campaign designed to further stress the resilience of such devices to simplistic spoofing attacks and highlight their actual vulnerability. We then propose an effective spoofing detection technique, that exploits the spatial and temporal correlation of the counterfeit signals by leveraging the statistical analysis of raw GNSS measurements. By not requiring access to the low signal processing level of the GNSS receiver, the proposed solution applies to any device embedding a GNSS receiver that provides raw GNSS measurements, such as current Android smartphones. Vulnerability analysis and validation of the proposed technique were conducted in a controlled environment by transmitting realistic, counterfeit Global Positioning System L1/CA navigation signals to a variety of Android smartphones embedding also different GNSS chipsets. We show that, under proper conditions, the devices were vulnerable to the attacks and that the effects were visible through their raw measurements, i.e., Carrier-to-noise ratio
$(C/N_{0})$
, pseudo-range measurements, and position estimates. In particular, the study demonstrates that cross-correlation between the
$C/N_{0}$
time series provided by each device for different GNSS satellites increases under spoofing conditions, thus constituting an effective metric to detect the attack within a few seconds.