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Global Navigation Satellite Systems (GNSS) are integrated into many devices.However, civilian GNSS signals are usually not cryptographically protected.This makes attacks that forge signals relatively easy. Considering moderndevices often have network connections and onboard sensors, the proposed hereProbabilistic Detection of GNSS Spoofing (PDS) scheme is based on suchopportunistic information. PDS has at its core two parts. First, a regressionproblem with motion model constraints, which equalizes the noise of alllocations considering the motion model of the device. Second, a Gaussianprocess, that analyzes statistical properties of location data to constructuncertainty. Then, a likelihood function, that fuses the two parts, as a basisfor a Neyman-Pearson lemma (NPL)-based detection strategy. Our experimentalevaluation shows a performance gain over the state-of-the-art, in terms ofattack detection effectiveness.

Probabilistic Detection of GNSS Spoofing using Opportunistic Information