Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation
Document Type
Conference Proceeding
Publication Date
5-29-2024
Abstract
The pervasive use of WiFi has driven the recent research in WiFi sensing, converting communication tech into sensing for applications such as activity recognition, user authentication, and vital sign monitoring. Despite the integration of deep learning into WiFi sensing systems, potential security vulnerabilities to adversarial attacks remain unexplored. This paper introduces the first physical attack focusing on deep learning-based WiFi sensing systems, demonstrating how adversaries can subtly manipulate WiFi packet preambles to affect channel state information (CSI), a critical feature in such systems, and thereby influence underlying deep learning models without disrupting regular communication. To realize the proposed attack in practical scenarios, we rigorously analyze and derive the intricate relationship between the pilot symbol and CSI. A novel mechanism is proposed to facilitate quantitive control of receiver-side CSI through minimal modifications to the pilot symbols of WiFi packets at the transmitter. We further develop a perturbation optimization method based on the Carlini & Wagner (CW) attack and a penalty-based training process to ensure the attack’s universal efficacy across various CSI responses and noise. The physical attack is implemented and evaluated in two representative WiFi sensing systems (i.e., activity recognition and user authentication) with 35 participants over 3 months. Extensive experiments demonstrate the remarkable attack success rates of 90.47% and 83.83% for activity recognition and user authentication, respectively.
Identifier
85206378681 (Scopus)
ISBN
[9798400704895]
Publication Title
ACM MobiCom 2024 - Proceedings of the 30th International Conference on Mobile Computing and Networking
External Full Text Location
https://doi.org/10.1145/3636534.3649367
First Page
373
Last Page
387
Grant
CNS2120276
Fund Ref
National Science Foundation
Recommended Citation
Li, Changming; Xu, Mingjing; Du, Yicong; Liu, Limin; Shi, Cong; Wang, Yan; Liu, Hongbo; and Chen, Yingying, "Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation" (2024). Faculty Publications. 405.
https://digitalcommons.njit.edu/fac_pubs/405
