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GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems
College of Computing, Mohammed VI Polytechnic University, Ben Guerir, Morocco.ORCID iD: 0000-0001-8042-3617
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0750-6309
College of Computing, Mohammed VI Polytechnic University, Ben Guerir, Morocco.ORCID iD: 0000-0002-7872-4469
Department of Telecommunications, Lviv Polytechnic National University, Lviv, Ukraine.
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2024 (English)In: IEEE Transactions on Machine Learning in Communications and Networking, E-ISSN 2831-316X, Vol. 2, p. 210-220Article in journal (Refereed) Published
Abstract [en]

Generative adversarial networks (GANs), a category of deep learning models, have become a cybersecurity concern for wireless communication systems. These networks enable potential attackers to deceive receivers that rely on convolutional neural networks (CNNs) by transmitting deceptive wireless signals that are statistically indistinguishable from genuine ones. While GANs have been used before for digitally modulated single-carrier waveforms, this study explores their applicability to model filtered multi-carrier waveforms, such as orthogonal frequency-division multiplexing (OFDM), filtered orthogonal FDM (F-OFDM), generalized FDM (GFDM), filter bank multi-carrier (FBMC), and universal filtered MC (UFMC). In this research, an evasion attack is conducted using GAN-generated counterfeit filtered multi-carrier signals to trick the target receiver. The results show that there is a remarkable 99.7% probability of the receiver misclassifying these GAN-based fabricated signals as authentic ones. This highlights the need for urgent investigation into the development of preventive measures to address this concerning vulnerability.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 2, p. 210-220
Keywords [en]
Conditional generative adversarial networks, deep convolutional neural networks, evasion attack, filtered multicarrier waveforms
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-201155DOI: 10.1109/tmlcn.2024.3361834OAI: oai:DiVA.org:liu-201155DiVA, id: diva2:1840499
Available from: 2024-02-23 Created: 2024-02-23 Last updated: 2025-02-07Bibliographically approved

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Singh Gaba, GurjotGurtov, Andrei

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