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A hybrid training-time and run-time defense against adversarial attacks in modulation classification

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posted on 2022-03-17, 09:45 authored by Lu Zhang, Sangarapillai LambotharanSangarapillai Lambotharan, Gan Zheng, Guisheng Liao, Ambra Demontis, Fabio Roli
Motivated 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 Letters

Volume

11

Issue

6

Pages

1161 - 1165

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher 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

2022-03-09

Publication date

2022-03-15

Copyright date

2022

ISSN

2162-2337

eISSN

2162-2345

Language

  • en

Depositor

Prof Sangarapillai Lambotharan. Deposit date: 16 March 2022