Probabilistic Detection of GNSS Spoofing using Opportunistic Information

Wenjie Liu, Panos Papadimitratos

Abstract: 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. Index Terms—Secure localization, GNSS spoofing detection, opportunistic information
Published in: 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 24 - 27, 2023
Hyatt Regency Hotel
Monterey, CA
Pages: 580 - 591
Cite this article: Liu, Wenjie, Papadimitratos, Panos, "Probabilistic Detection of GNSS Spoofing using Opportunistic Information," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 580-591. https://doi.org/10.1109/PLANS53410.2023.10139976
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