BANA: Body Area Network Authentication Exploiting Channel Characteristics
Author(s): Lu Shi, Ming Li, Shucheng Yu, Jiawei Yuan

Date: April 2012
Publication: ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec '12
Page(s): 27 - 38
Publisher: ACM
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Abstract or Summary:
Wireless body area network (BAN) is a promising technology for real-time monitoring of physiological signals to support medical applications. In order to ensure the trustworthy and reliable gathering of patient's critical health information, it is essential to provide node authentication service in a BAN, which prevents an attacker from impersonation and false data/command injection. Although quite fundamental, the authentication in BAN still remains a challenging issue. On one hand, traditional authentication solutions depend on prior trust among nodes whose establishment would require either key pre-distribution or non-intuitive participation by inexperienced users, while they are vulnerable to key compromise. On the other hand, most existing non-cryptographic authentication schemes require advanced hardware capabilities or significant modifications to the system software, which are impractical for BANs.

In this paper, for the first time, we propose a lightweight body area network authentication scheme (BANA) that does not depend on prior-trust among the nodes and can be efficiently realized on commercial off-the-shelf low-end sensor devices. This is achieved by exploiting physical layer characteristics unique to a BAN, namely, the distinct received signal strength (RSS) variation behaviors between an on-body communication channel and an off-body channel. Our main finding is that the latter is more unpredictable over time, especially under various body motion scenarios. This unique channel characteristic naturally arises from the multi-path environment surrounding a BAN, and cannot be easily forged by attackers. We then adopt clustering analysis to differentiate the signals from an attacker and a legitimate node. The effectiveness of BANA is validated through extensive real-world experiments under various scenarios. It is shown that BANA can accurately identify multiple attackers with minimal amount of overhead.

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