Brain-Inspired GNSS Spoofing Attack Cognition Using Spiking Neural Network for Train Positioning

Abstract

Global Navigation Satellite System (GNSS) has been an exciting growing research field in railway transportation due to its accuracy, low cost, and global availability. However, GNSS-based train positioning is susceptible to spoofing attacks in complex railway operation environments due to GNSS vulnerabilities. Aiming at resilience assurance of train positioning using GNSS, this paper proposes a spoofing attack cognition solution based on brain-inspired intelligence. Specifically, a multi-domain joint Spoofing Detector (SD) is proposed based on the Spiking Neural Network (SNN) to perceive the cybersecurity threat from spoofing attacks. Furthermore, the active spoofing protection solution is designed using a brain-inspired cognition approach considering different spoofing attack situation phases. An Attack Situation Recognizer (ASR) network combining signal decomposition and identification is proposed, which features SNN-optimized residual blocks and the cross-attention fusion modules, leading to enhanced recognition capability at a low computational cost level. Experiments based on field data and a GNSS spoofing injection testbench platform demonstrate the performance of this solution in spoofing attack identification and situation cognition over different referencing methods. The results illustrate that the proposed solution aligns with the railway Positioning, Navigation & Timing (PNT) resilience requirement by leveraging the advantages of brain-like intelligence in enhancing the trustworthiness of GNSS positioning in railway transportation systems.