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

PQConnect: Automated Post-Quantum End-to-End Tunnels

Daniel J. Bernstein (University of Illinois at Chicago and Academia Sinica), Tanja Lange (Eindhoven University of Technology amd Academia Sinica), Jonathan Levin (Academia Sinica and Eindhoven University of Technology), Bo-Yin Yang (Academia Sinica)

Read More

Careful About What App Promotion Ads Recommend! Detecting and...

Shang Ma (University of Notre Dame), Chaoran Chen (University of Notre Dame), Shao Yang (Case Western Reserve University), Shifu Hou (University of Notre Dame), Toby Jia-Jun Li (University of Notre Dame), Xusheng Xiao (Arizona State University), Tao Xie (Peking University), Yanfang Ye (University of Notre Dame)

Read More

Mens Sana In Corpore Sano: Sound Firmware Corpora for...

René Helmke (Fraunhofer FKIE), Elmar Padilla (Fraunhofer FKIE, Germany), Nils Aschenbruck (University of Osnabrück)

Read More

Speak Up, I’m Listening: Extracting Speech from Zero-Permission VR...

Derin Cayir (Florida International University), Reham Mohamed Aburas (American University of Sharjah), Riccardo Lazzeretti (Sapienza University of Rome), Marco Angelini (Link Campus University of Rome), Abbas Acar (Florida International University), Mauro Conti (University of Padua), Z. Berkay Celik (Purdue University), Selcuk Uluagac (Florida International University)

Read More