Spoofing Detection for LiDAR in Autonomous Vehicles: A Physical-Layer Approach

Recent years have witnessed the ever-growing interest and adoption of autonomous vehicles (AVs), thanks to the latest advancement in sensing and artificial intelligence (AI) technologies. The LiDAR sensor is adopted by most AV manufacturers for its high precision and high reliability. Unfortunately, LiDARs are susceptible to malicious spoofing attacks, which can lead to severe safety consequences for AVs. Most current work focuses on protecting LiDAR against spoofing attacks by using perception model-level defense methods, whose effectiveness unfortunately depends on the correctness of the LiDAR’s sensing outcome. A spoofer thus can elude from these methods as long as it fabricates points that maintain the right contextual relationship held by the legitimate points. In this paper, we propose to use the signal’s Doppler frequency shift to verify the sender of the signal and detect potential spoofing attacks. To this end, we first thoroughly analyze the working principle of LiDAR and conduct real-world experiments to deeply understand and reveal the vulnerability of LiDAR sensors. We then prove that the Doppler frequency shifts of legitimate and spoofing signals present different characteristics, which can be used to fundamentally protect the LiDAR sensing outcome. For better demonstration purposes, we consider three attack models, including static attacker, moving attacker, and moving attacker with control of both velocity and signal frequency. For each of the models, we first show how the spoofing attack is performed and then present our countermeasures. We then propose a statistical spoofing detection framework to jointly consider the impact of short-term uncertainty in vehicle velocity, which can provide more accurate spoofing detection results in realistic environments. Extensive numerical results are provided in a wide range of settings and road conditions.