Final_version_HTRD_Lu_Zhang.pdf (1.25 MB)
Download fileA hybrid training-time and run-time defense against adversarial attacks in modulation classification
journal contribution
posted on 2022-03-17, 09:45 authored by Lu Zhang, Sangarapillai LambotharanSangarapillai Lambotharan, Gan Zheng, Guisheng Liao, Ambra Demontis, Fabio RoliMotivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this paper, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies.
Funding
Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)
Engineering and Physical Sciences Research Council
Find out more...Unlocking Potentials of MIMO Full-duplex Radios for Heterogeneous Networks (UPFRONT)
Engineering and Physical Sciences Research Council
Find out more...History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IEEE Wireless Communications LettersVolume
11Issue
6Pages
1161 - 1165Publisher
Institute of Electrical and Electronics Engineers (IEEE)Version
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2022-03-09Publication date
2022-03-15Copyright date
2022ISSN
2162-2337eISSN
2162-2345Publisher version
Language
- en