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This paper introduces a causal high-order liquid evidence framework for detecting gradual GNSS spoofing attacks in autonomous vehicles, addressing the limitations of existing methods that rely on static features. By constructing a physics-guided inconsistency residual and processing multiple evidence streams through adaptive liquid encoders, the approach captures the evolution of GNSS-implied displacement versus onboard motion. Experimental results demonstrate superior performance, achieving F1-scores of 0.9535 and 0.9777 on real-world datasets, with rapid detection of spoofing transitions.
A novel framework achieves up to 97.77% accuracy in detecting subtle GNSS spoofing attacks, outperforming traditional methods.
Accurate Global Navigation Satellite System (GNSS)-based localization is essential for safe and reliable autonomous driving. However, spoofing attacks can manipulate vehicle position estimates. Continuous and subtle attacks are particularly difficult to detect because individual GNSS observations may remain plausible while the inconsistency between GNSS-implied displacement and onboard vehicle motion gradually increases. Existing methods often rely on static vehicle-behavior features or a single residual signal and do not explicitly model this evolution. To address this problem, we propose a causal high-order liquid evidence framework for GNSS spoofing detection. The method first constructs a physics-guided GNSS--motion inconsistency residual by comparing GNSS-implied displacement with onboard-motion-derived displacement. It then forms separate evidence streams for the residual level and its first- and second-order discrete variations, with relevant contextual cues selected according to the evidence order. Each stream is processed by a separate adaptive liquid encoder, and the resulting temporal states are hierarchically coupled to predict spoofing at the window endpoint using only current and past observations. Experiments on three subsets of the real-world AV-GPS dataset show that the proposed method achieves the highest F1-scores among the evaluated temporal models on Dataset~1 and Dataset~3, reaching 0.9535 and 0.9777, respectively. On Dataset~3, it detects both labeled normal-to-attack transitions within four sampling steps. Code and datasets are publicly available at: https://github.com/pangjunbiao/GNSS_Spoofing.git.
