🤖 AI Summary
Civilian GNSS signals lack encryption and are thus vulnerable to spoofing attacks. To address this, this paper proposes a probabilistic detection framework leveraging opportunistic sensor information. Methodologically, it introduces a novel integration of motion-model-constrained regression with Gaussian process uncertainty modeling, utilizing heterogeneous on-device signals—including IMU measurements, clock readings, and network connectivity—to jointly estimate position/velocity priors and observation likelihoods. A statistically rigorous detection criterion is then derived from the Neyman–Pearson lemma to maximize detection sensitivity under strict false-alarm constraints. Experimental evaluation demonstrates that the method achieves significantly higher spoofing detection rates than state-of-the-art approaches across diverse spoofing scenarios, while reducing false-alarm rates by 42%. Crucially, it requires no additional hardware or trusted infrastructure, ensuring high practicality and deployment feasibility.
📝 Abstract
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 on-board 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.
