mmSpoof: Resilient Spoofing of Automotive Millimeter-wave Radars using Reflect Array


FMCW radars are integral to automotive driving for robust and weather-resistant sensing of surrounding objects. However, these radars are vulnerable to spoofing attacks that can cause sensor malfunction and potentially lead to accidents. Previous attempts at spoofing FMCW radars using an attacker device have not been very effective due to the need for synchronization between the attacker and the victim. We present a novel spoofing mechanism called mmSpoof that does not require synchronization and is resilient to various security features and countermeasures of the victim radar. Our spoofing mechanism uses a “reflect array” based attacker device that reflects the radar signal with appropriate modulation to spoof the victim’s radar. We provide insights and mechanisms to flexibly spoof any distance and velocity on the victim’s radar using a unique frequency shift at the mmSpoof’s reflect array. We design a novel algorithm to estimate this frequency shift without assuming prior information about the victim’s radar. We show the effectiveness of our spoofing using a compact and mobile setup with commercial-off-the-shelf components in realistic automotive driving scenarios with commercial radars.

In 2023 IEEE Symposium on Security and Privacy (SP)
Kshitiz Bansal
Kshitiz Bansal
Radar Engineer

My research interests include Radar Imaging, Computer Vision and Deep Learning.