Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks

Deep learning technologies have seen widespread adoption in WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, which has received little attention within the WiFi sensing community. To more comprehensively understand the potential threats posed by perturbation attacks, we present a novel attack method, named WiIntruder, distinguishing itself with universality, robustness, and stealthiness. This paper intends to provide a catalyst that promotes the assessment of security in existing WiFi-based sensing systems. We achieve the three aforementioned salient features in WiIntruder through the following three steps: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, thereby enabling a universal perturbation attack vector applicable to a wide range of applications; (2) Mitigating the impact of perturbation signal distortion by optimizing key factors of device synchronization and wireless propagation through a heuristic particle swarm algorithm; and (3) Enhancing the diversity and stealthiness of attack patterns by randomly switching among perturbation surrogates generated by a generative adversarial network. Experimental results confirm the threat posed by WiIntruder to four common WiFi-based services, with the average accuracy decrease by 72.9% under black-box attack scenarios. © 2004-2012 IEEE.