  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 modern
devices often have network connections and onboard sensors, the proposed here
Probabilistic Detection of GNSS Spoofing (PDS) scheme is based on such
opportunistic information. PDS has at its core two parts. First, a regression
problem with motion model constraints, which equalizes the noise of all
locations considering the motion model of the device. Second, a Gaussian
process, that analyzes statistical properties of location data to construct
uncertainty. Then, a likelihood function, that fuses the two parts, as a basis
for a Neyman-Pearson lemma (NPL)-based detection strategy. Our experimental
evaluation shows a performance gain over the state-of-the-art, in terms of
attack detection effectiveness.
