  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 salient features: (1) Maximizing transferability by
differentiating user-state-specific feature spaces across sensing models,
leading to a universally effective perturbation attack applicable to common
applications; (2) Addressing perturbation signal distortion caused by device
synchronization and wireless propagation when critical parameters are optimized
through a heuristic particle swarm-driven perturbation generation algorithm;
and (3) Enhancing attack pattern diversity and stealthiness through random
switching of perturbation surrogates generated by a generative adversarial
network. Extensive experimental results confirm the practical threats of
perturbation attacks to common WiFi-based services, including user
authentication and respiratory monitoring.
