Physical Layer Authentication and Security Design in the Machine Learning Era

Hoang, TM; Vahid, A; Tuan, HD; Hanzo, L

Physical Layer Authentication and Security Design in the Machine Learning Era

Hoang, TM Vahid, A Tuan, HD Hanzo, L

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Communications Surveys and Tutorials, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

In Progress

	Filename	Description	Size
	1722089.pdf	Published version	1.39 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is being processed and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Hoang, TM
dc.contributor.author	Vahid, A
dc.contributor.author	Tuan, HD
dc.contributor.author	Hanzo, L
dc.date.accessioned	2024-08-21T05:41:58Z
dc.date.available	2024-08-21T05:41:58Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Communications Surveys and Tutorials, 2024, PP, (99), pp. 1-1
dc.identifier.issn	1553-877X
dc.identifier.issn	1553-877X
dc.identifier.uri	http://hdl.handle.net/10453/180505
dc.description.abstract	Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term “PHY security” is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat NNs as special types of ML and present how to deal with PHY security optimization problems using NNs. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Communications Surveys and Tutorials
dc.relation.isbasedon	10.1109/COMST.2024.3363639
dc.rights	info:eu-repo/semantics/restrictedAccess
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	Physical Layer Authentication and Security Design in the Machine Learning Era
dc.type	Journal Article
utslib.citation.volume	PP
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	in_progress	*
dc.date.updated	2024-08-21T05:41:56Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term “PHY security” is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat NNs as special types of ML and present how to deal with PHY security optimization problems using NNs. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180505