Jung-Woo Chang (University of California, San Diego), Ke Sun (University of California, San Diego), Nasimeh Heydaribeni (University of California, San Diego), Seira Hidano (KDDI Research, Inc.), Xinyu Zhang (University of California, San Diego), Farinaz Koushanfar (University of California, San Diego)

Machine Learning (ML) has been instrumental in enabling joint transceiver optimization by merging all physical layer blocks of the end-to-end wireless communication systems. Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer protocols, and wireless domain constraints. This paper proposes Magmaw, a novel wireless attack methodology capable of generating universal adversarial perturbations for any multimodal signal transmitted over a wireless channel. We further introduce new objectives for adversarial attacks on downstream applications. We adopt the widely used defenses to verify the resilience of Magmaw. For proof-of-concept evaluation, we build a real-time wireless attack platform using a software-defined radio system. Experimental results demonstrate that Magmaw causes significant performance degradation even in the presence of strong defense mechanisms. Furthermore, we validate the performance of Magmaw in two case studies: encrypted communication channel and channel modality-based ML model. Our code is available at https://github.com/juc023/Magmaw.

View More Papers

What’s Done Is Not What’s Claimed: Detecting and Interpreting...

Chang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Zhixiu Guo (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Jun Dai, Xiaoyan Sun (Department of Computer Science, Worcester Polytechnic Institute), Yi Yang (Institute of Information Engineering, Chinese Academy…

Read More

Trust or Bust: A Survey of Threats in Decentralized...

Hetvi Shastri (University of Massachusetts Amherst), Akanksha Atrey (Nokia Bell Labs), Andre Beck (Nokia Bell Labs), Nirupama Ravi (Nokia Bell Labs)

Read More

Home Shield IoT Traffic Analyzer: A Comprehensive Analysis of...

Dhananjai Bajpai (Marquette University), Keyang Yu (Marquette University)

Read More

Privacy-Enhancing Technologies Against Physical-Layer and Link-Layer Device Tracking: Trends,...

Apolline Zehner (Universite libre de Bruxelles), Iness Ben Guirat (Universite libre de Bruxelles), Jan Tobias Muhlberg (Universite libre de Bruxelles)

Read More