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Wireless Networks
Erschienen in: Wireless Networks 7/2023

22.03.2023

Adversarial defense method based on ensemble learning for modulation signal intelligent recognition

verfasst von: Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Jian Chen, Bin Yan

Erschienen in: Wireless Networks | Ausgabe 7/2023

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Abstract

Modulation signal intelligent recognition model based on deep learning is widely used in the field of radio signal intelligent processing, but the adversarial attack has become a huge security threat. In order to promote the safe and reliable application of the modulation recognition intelligent model, it is necessary to study its adversarial defense technology. An adversarial defense method based on ensemble learning for modulation signal intelligent recognition model is proposed in this paper. Specifically, this method is achieved by combining multiple defense models such as adversarial training, defensive distillation, and noise smoothing. Variety of attack algorithms in both the white-box and black-box scenarios under different intensities of perturbation and different signal-to-noise ratios are carried out to verify the robustness performance of the proposed method. Strikingly, the accuracy of the model is improved to over 80% when the SNR is above 0 dB under Carlini and Wagner attack.
Vorheriger Artikel Slice combination convolutional neural network based radio frequency fingerprint identification for Internet of Things
Nächster Artikel Abnormal nodes sensing model in regional wireless networks based on convolutional neural network

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Metadaten
Titel
Adversarial defense method based on ensemble learning for modulation signal intelligent recognition
verfasst von
Chao Han
Ruoxi Qin
Linyuan Wang
Weijia Cui
Jian Chen
Bin Yan
Publikationsdatum
22.03.2023
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 7/2023
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-023-03299-4

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