Deep learning aided physical-layer security: the security versus reliability trade-off
Deep learning aided physical-layer security: the security versus reliability trade-off
This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement. The security of the communication system is optimized using deep neural networks (DNNs). More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and the discrete-input continuous-output memoryless channel (DCMC) capacities. A pair of loss functions were defined by relying on the Lagrangian and on the monotonic-function based techniques. These were then used for managing the learning/training process of the DNNs for finding near-optimal solutions to the associated non-convex problem. The Lagrangian technique was shown to approach the performance of the exhaustive search. We concluded by characterizing the security vs reliability trade-off in terms of the intercept probability vs the outage probability.
Array signal processing, Channel capacity, Channel estimation, Deep learning, Lagrange., Optimization, Physical layer security, Reliability, Security, deep learning, neural network, reliability
Hoang, Minh Tiep
79ed4c0b-02ee-420a-a4cf-eeb0c2715d76
Liu, Dong
7a88f69c-c83c-4837-bf7a-49879392c32f
Luong, Thien Van
e95afcc7-65ce-46b2-a002-d36e24c90b97
Zhang, Jiankang
c6c025b3-6576-4f9d-be95-57908e61fa88
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
27 December 2021
Hoang, Minh Tiep
79ed4c0b-02ee-420a-a4cf-eeb0c2715d76
Liu, Dong
7a88f69c-c83c-4837-bf7a-49879392c32f
Luong, Thien Van
e95afcc7-65ce-46b2-a002-d36e24c90b97
Zhang, Jiankang
c6c025b3-6576-4f9d-be95-57908e61fa88
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Hoang, Minh Tiep, Liu, Dong, Luong, Thien Van, Zhang, Jiankang and Hanzo, Lajos
(2021)
Deep learning aided physical-layer security: the security versus reliability trade-off.
IEEE Transactions on Cognitive Communications and Networking.
(doi:10.1109/TCCN.2021.3138392).
Abstract
This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement. The security of the communication system is optimized using deep neural networks (DNNs). More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and the discrete-input continuous-output memoryless channel (DCMC) capacities. A pair of loss functions were defined by relying on the Lagrangian and on the monotonic-function based techniques. These were then used for managing the learning/training process of the DNNs for finding near-optimal solutions to the associated non-convex problem. The Lagrangian technique was shown to approach the performance of the exhaustive search. We concluded by characterizing the security vs reliability trade-off in terms of the intercept probability vs the outage probability.
Text
CAMERA_READY_TCCN
- Accepted Manuscript
More information
Accepted/In Press date: 21 December 2021
Published date: 27 December 2021
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Array signal processing, Channel capacity, Channel estimation, Deep learning, Lagrange., Optimization, Physical layer security, Reliability, Security, deep learning, neural network, reliability
Identifiers
Local EPrints ID: 453295
URI: http://eprints.soton.ac.uk/id/eprint/453295
ISSN: 2332-7731
PURE UUID: 90b9cecf-cf91-4a49-9c71-f972a8eb0e81
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Date deposited: 12 Jan 2022 17:35
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Minh Tiep Hoang
Author:
Dong Liu
Author:
Thien Van Luong
Author:
Jiankang Zhang
Author:
Lajos Hanzo
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