Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

Wang, Y; Sun, T; Li, S; Yuan, X; Ni, W; Hossain, E; Vincent Poor, H

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

Wang, Y Sun, T Li, S Yuan, X

Ni, W

Hossain, E Vincent Poor, H

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Communications Surveys and Tutorials, 2023, 25, (4), pp. 2245-2298
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Sun, T
dc.contributor.author	Li, S
dc.contributor.author	Yuan, X https://orcid.org/0000-0002-9167-1613
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Hossain, E
dc.contributor.author	Vincent Poor, H
dc.date.accessioned	2024-05-03T00:44:17Z
dc.date.available	2024-05-03T00:44:17Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Communications Surveys and Tutorials, 2023, 25, (4), pp. 2245-2298
dc.identifier.issn	1553-877X
dc.identifier.issn	1553-877X
dc.identifier.uri	http://hdl.handle.net/10453/178561
dc.description.abstract	Adversarial attacks and defenses in machine learning and deep neural network (DNN) have been gaining significant attention due to the rapidly growing applications of deep learning in communication networks. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on DNN-based classification models for communication applications. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Communications Surveys and Tutorials
dc.relation.isbasedon	10.1109/COMST.2023.3319492
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey
dc.type	Journal Article
utslib.citation.volume	25
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-03T00:44:11Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	4

Abstract:

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have been gaining significant attention due to the rapidly growing applications of deep learning in communication networks. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on DNN-based classification models for communication applications. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.

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http://hdl.handle.net/10453/178561