Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2019-12, Vol.15 (12), p.6481-6491
Main Authors: Pan, Fei, Pang, Zhibo, Wen, Hong, Luvisotto, Michele, Xiao, Ming, Liao, Run-Fa, Chen, Jie
Format: Article
Language:English
Subjects:
Artificial intelligence
Authentication
Channel estimation
Communication system security
Computer simulation
Cyber-physical security
Cyber-physical systems
Decision making
Machine learning
Performance enhancement
physical layer authentication (PHY-AUC)
Prediction algorithms
supervised machine learning (ML)
Training
Wireless communication
Wireless sensor networks
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Summary:Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.
ISSN:1551-3203
1941-0050
1941-0050
DOI:10.1109/TII.2019.2925418