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  • SIGPRO-D-19-00145R1(1)

    Rights statement: This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 166, 2020 DOI: 10.1016/j.sigpro.2019.107272

    Accepted author manuscript, 741 KB, PDF document

    Embargo ends: 27/08/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Physical Layer Authentication under Intelligent Spoofing in Wireless Sensor Networks

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Article number107272
<mark>Journal publication date</mark>1/01/2020
<mark>Journal</mark>Signal Processing
Volume166
Number of pages13
Publication statusPublished
Early online date27/08/19
Original languageEnglish

Abstract

Location based access in wireless sensor networks (WSN) are vulnerable to location spoofing attacks. In this paper, we investigate the physical layer (PHY-layer) authentication in the threat of an intelligent location spoofing attack. The intelligent attack can emulate the legitimate channel information and maximize its long-term cumulative reward. First, we analyze the feasibility of this intelligent attack and investigate how it threats to the networks. Specifically, we derive the optimal transmit power allocation and find the worst case for the defenders, namely optimal intelligent attack, in which the attacker can learn the intelligent attack action based on the beamforming with optimal transmit power allocation. To defend against such an intelligent attack with high accuracy and low overhead, we develop a cooperative PHY-layer authentication scheme. Then, we provide an in-depth analysis on the belief and derive the belief bounds and the closed-form expression for the belief threshold. Furthermore, considering the whole computation complexity and the double counting problem in a loopy graph, we propose the cooperative neighbour selection algorithm to accelerate belief convergence and reduce the overhead. Finally, the simulation results reveal that the proposed method can significantly improve the defense performance compared with the state-of-art methods.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 166, 2020 DOI: 10.1016/j.sigpro.2019.107272