Physical layer authentication and security design in the machine learning era
Physical layer authentication and security design in the machine learning era
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.
Authentication, Communication system security, Jamming, Machine Learning, Neural Network, Neural networks, Optimization, Physical Layer Security, Secure Transmission, Security, Wireless communication
1
M. Hoang, Tiep
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Vahid, Alireza
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Tuan, Hoang Duong
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Hanzo, Lajos
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M. Hoang, Tiep
a23cb1a4-599b-4022-aeab-dfac681c9803
Vahid, Alireza
ea0ee5e6-52ca-44b4-a3ec-1dcd8a8ab7a3
Tuan, Hoang Duong
423ee18d-ebc7-44d9-9264-3819b63779eb
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
M. Hoang, Tiep, Vahid, Alireza, Tuan, Hoang Duong and Hanzo, Lajos
(2024)
Physical layer authentication and security design in the machine learning era.
IEEE Communications Surveys & Tutorials, .
(doi:10.1109/COMST.2024.3363639).
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.
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More information
Accepted/In Press date: 31 January 2024
e-pub ahead of print date: 8 February 2024
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Authentication, Communication system security, Jamming, Machine Learning, Neural Network, Neural networks, Optimization, Physical Layer Security, Secure Transmission, Security, Wireless communication
Identifiers
Local EPrints ID: 486776
URI: http://eprints.soton.ac.uk/id/eprint/486776
ISSN: 1553-877X
PURE UUID: c45c6ab3-0a6c-40be-a7a9-bd0f21acb333
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Date deposited: 06 Feb 2024 17:34
Last modified: 24 Apr 2024 04:04
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Contributors
Author:
Tiep M. Hoang
Author:
Alireza Vahid
Author:
Hoang Duong Tuan
Author:
Lajos Hanzo
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