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Automated Labeling and Learning for Physical Layer Authentication against Clone Node and Sybil Attacks in Industrial Wireless Edge Networks

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journal contribution
posted on 08.03.2021, 23:38 by S Chen, Z Pang, H Wen, Kan Yu, T Zhang, Y Lu
© 2005-2012 IEEE. In this article, a scheme to detect both clone and Sybil attacks by using channel-based machine learning is proposed. To identify malicious attacks, channel responses between sensor peers have been explored as a form of fingerprints with spatial and temporal uniqueness. Moreover, the machine-learning-based method is applied to provide a more accurate authentication rate. Specifically, by combining with edge devices, we apply a threshold detection method based on channel differences to provide offline training sample sets with labels for the machine learning algorithm, which avoids manually generating labels. Therefore, our proposed scheme is lightweight for resource constrained industrial wireless devices, since only an online-decision making is required. Extensive simulations and experiments were conducted in real industrial environments. Both results show that the authentication accuracy rate of our strategy with an appropriate threshold can achieve 84% without manual labeling.

Funding

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61572114, in part by the National Major R&D Program (2018YFB0904900 and 2018YFB0904905), in part by Sichuan Science and Technology Service Development Project (18KJFWSF0368), and in part by Sichuan Science and Technology Basic Research Condition Platform Project (2018TJPT0041).

History

Publication Date

01/03/2021

Journal

IEEE Transactions on Industrial Informatics

Volume

17

Issue

3

Pagination

(p. 2041-2051)

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

ISSN

1551-3203

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