Detection of Replay Attacks to GNSS based on Partial Correlations and Authentication Data Unpredictability
Gonzalo Seco-Granados, David Gomez-Casco, Jose A. Lopez-Salcedo,, Ignacio Fernandez-Hernandez

TL;DR
This paper introduces five novel detectors for GNSS spoofing, leveraging partial correlations and authentication data unpredictability, demonstrating high detection probability even against sophisticated replay attacks in various conditions.
Contribution
The work proposes and evaluates five new detectors based on partial correlations and OSNMA data, with one showing superior performance in detecting replay spoofing attacks.
Findings
One detector consistently outperforms others in detection accuracy.
High detection probability achieved even under favorable spoofing conditions.
Practical implementation considerations for Galileo OSNMA are discussed.
Abstract
Intentional interference, and in particular GNSS spoofing, is currently one of the most significant concerns of the Positioning, Navigation and Timing (PNT) community. With the adoption of Open Service Navigation Message Authentication (OSNMA) in Galileo, the E1B signal component will continuously broadcast unpredictable cryptographic data. This allows GNSS receivers not only to ensure the authenticity of data origin but also to detect replay spoofing attacks for receivers already tracking real signals with relatively good visibility conditions. Since the spoofer needs to estimate the unpredictable bits introduced by OSNMA with almost zero delay in order to perform a Security Code Estimation and Replay (SCER) attack, the spoofer unavoidably introduces a slight distortion into the signal, which can be the basis of a spoofing detector. In this work, we propose five detectors based on…
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