ActListener: Imperceptible Activity Surveillance by Pervasive Wireless Infrastructures
Published in IEEE International Conference on Distributed Computing Systems (IEEE ICDCS 2022), 2022
Recommended citation: Li Lu*, Zhongjie Ba, Feng Lin, Jinsong Han, Kui Ren. "ActListener: Imperceptible Activity Surveillance by Pervasive Wireless Infrastructures." Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS). Bologna, Italy. pp. 776-786. 2022. doi: 10.1109/ICDCS54860.2022.00080.
IEEE International Conference on Distributed Computing Systems is a premier international conference in distrubited computing and communication areas. IEEE ICDCS is also a CCF-B conference.
Presentation Venue: Session 2B (IoT Applications and Security, IoT), IEEE ICDCS 2022 @ Bologna, Italy in Jul. 11, 2022. Due to the epidemic prevention policy of COVID-19 in China, I did not attend the IEEE ICDCS 2022 held in Bologna, Italy. I made the slide for the paper presentation, and my labmate@Rutgers, Wenjin Zhang, presented the paper on behalf of me.
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 is leaked to adversaries for surveillance in all probability. To reveal the threat, this paper demonstrates ActListener, which could eavesdrop on user activities imperceptibly using a WiFi infrastructure in any location of user sensing area. The proposed attack requires no direct physical access to the victim user’s devices and prior knowledge of activity recognition model details and device locations. In particular, ActListener first detects the signal segment induced by each human activity, and estimates the locations of legitimate devices and the victim users relative to the adversary’s device for further signal modeling. Then, ActListener models propagating WiFi signals to construct the relationship between physical locations and received signals, and converts the eavesdropped signals to that by legitimate devices based on the models. Furthermore, a neural network-based generative model is designed to calibrate the converted signals for resisting noises in over-the-air WiFi signals.