Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems


Zikun Liu, Changming Xu, and Emerson Sie, University of Illinois Urbana-Champaign; Gagandeep Singh, University of Illinois Urbana-Champaign and VMware Research; Deepak Vasisht, University of Illinois Urbana-Champaign


Machine Learning (ML) is an increasingly popular tool for designing wireless systems, both for communication and sensing applications. We design and evaluate the impact of practically feasible adversarial attacks against such ML-based wireless systems. In doing so, we solve challenges that are unique to the wireless domain: lack of synchronization between a benign device and the adversarial device, and the effects of the wireless channel on adversarial noise. We build, RAFA (RAdio Frequency Attack), the first hardware-implemented adversarial attack platform against ML-based wireless systems, and evaluate it against two state-of-the-art communication and sensing approaches at the physical layer. Our results show that both these systems experience a significant performance drop in response to the adversarial attack

NSDI '23 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {286473,
author = {Zikun Liu and Changming Xu and Emerson Sie and Gagandeep Singh and Deepak Vasisht},
title = {Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems},
booktitle = {20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
year = {2023},
isbn = {978-1-939133-33-5},
address = {Boston, MA},
pages = {1801--1817},
url = {https://www.usenix.org/conference/nsdi23/presentation/liu-zikun},
publisher = {USENIX Association},
month = apr,

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