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Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS
Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China.;Sichuan Agr Univ, Coll Informat Engn, Yaan 625014, Peoples R China..
ABB Corp Res Ctr, Dept Automat Solut, Vasteras, Sweden..
Univ Elect Sci & Technol China, Dept Aeronaut & Astronaut, Chengdu 611731, Sichuan, Peoples R China..
ABB Corp Res Ctr, Dept Automat Solut, Vasteras, Sweden..
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2019 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, no 12, p. 6481-6491Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 15, no 12, p. 6481-6491
Keywords [en]
Authentication, Channel estimation, Wireless communication, Communication system security, Wireless sensor networks, Training, Prediction algorithms, Cyber-physical security, physical layer authentication (PHY-AUC), supervised machine learning (ML)
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-266432DOI: 10.1109/TII.2019.2925418ISI: 000502295800029Scopus ID: 2-s2.0-85073316937OAI: oai:DiVA.org:kth-266432DiVA, id: diva2:1385985
Note

QC 20200116

Available from: 2020-01-16 Created: 2020-01-16 Last updated: 2020-01-16Bibliographically approved

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