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Sensors 2016, 16(10), 1574; doi:10.3390/s16101574

Patterns-of-Life Aided Authentication

1
School of Electronic Engineering, Xidian University, Xi’an 710071, China
2
Centre for Wireless Research, University of Bedfordshire, Luton LU1 3JU, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Kamiar Aminian
Received: 12 June 2016 / Revised: 13 September 2016 / Accepted: 20 September 2016 / Published: 23 September 2016
(This article belongs to the Special Issue Body Worn Behavior Sensing)
View Full-Text   |   Download PDF [1644 KB, uploaded 23 September 2016]   |  

Abstract

Wireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer security arrangements are not sufficient to employ their full potential, and novel solutions are necessary. In contrast, security methods based on physical layers tend to be more suitable and have simple requirements. The problem of initial trust needs to be addressed as a prelude to the physical layer security key arrangement. This paper proposes a patterns-of-life aided authentication model to solve this issue. The model employs the wireless channel fingerprint created by the user’s behavior characterization. The performance of the proposed model is established through experimental measurements at 2.45 GHz. Experimental results show that high correlation values of 0.852 to 0.959 with the habitual action of the user in different scenarios can be used for auxiliary identity authentication, which is a scalable result for future studies. View Full-Text
Keywords: Wireless Body Area Networks; initial trust; patterns-of-life aided authentication Wireless Body Area Networks; initial trust; patterns-of-life aided authentication
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, N.; Ren, A.; Zhang, Z.; Zhu, T.; Rehman, M.U.; Yang, X.; Hu, F. Patterns-of-Life Aided Authentication. Sensors 2016, 16, 1574.

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