Physical layer plausibility checks for misbehavior detection in V2X networks

Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can circumvent application layer sanity checks. In this paper, we propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. These checks can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%, far outperforming recently proposed machine learning-based misbehavior detection methods operating at the application layer.