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

arXiv:2404.15587v1 Announce Type: new
Abstract: Deep learning technologies are pivotal in enhancing the performance of WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, and regrettably, there is lacking serious attention to this security issue within the WiFi sensing community. In this paper, we elaborate such an attack, called WiIntruder, distinguishing itself with universality, robustness, and stealthiness, which serves as a catalyst to assess the security of existing WiFi-based sensing systems. This attack encompasses the following …

adversarial analysis arxiv attacks attention community cs.cr deep learning issue performance security security analysis sensing serious systems technologies threats vulnerable wifi wireless