An Imperceptible Eavesdropping Attack on WiFi Sensing Systems

Abstract

Recent years have witnessed enormous research efforts on WiFi sensing to enable intelligent services of Internet of Things. However, due to the omni-directional broadcasting manner of WiFi signals, the activity semantic underlying the signals can be leaked to adversaries for surveillance, as demonstrated by our previous work. In this paper, we further extend the attack capability of ActListener to impersonation attack, which could eavesdrop on users' behavioral uniqueness imperceptibly using a WiFi infrastructure in any location of user sensing area. In particular, ActListener detects each human activity and converts the eavesdropped signals to that by legitimate devices based on our proposed signal propagation models. To extract noise-resilient individual behavioral uniqueness from converted CSI of WiFi signals, we further add user identification models into the substitute model set for training the signal pattern calibration generative model. Experimental results demonstrate that ActListener could achieve over 80% accuracy in activity semantics retrieval and impersonation by using the converted signals.