Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adversarial attacks. This paper delves into backdoor attacks targeting deep learning-enabled semantic communication systems. Since current works on backdoor attacks are not tailored for semantic communication scenarios, a new backdoor attack paradigm on semantic symbols (BASS) is introduced, based on which the corresponding defense measures are designed. Specifically, a training framework is proposed to prevent BASS. Additionally, reverse engineering-based and pruning-based defense strategies are designed to protect against backdoor attacks in semantic communication. Simulation results demonstrate the effectiveness of both the proposed attack paradigm and the defense strategies.
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Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems
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
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- Award ID(s):
- 2148583
- PAR ID:
- 10425814
- Date Published:
- Journal Name:
- USENIX Symposium on Networked Systems Design and Implementation
- Volume:
- 20
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
