Device Fingerprinting to Enhance Wireless Security using Nonparametric Bayesian Method

in Bayesian, fingerprinting, non-parametric, security
TitleDevice Fingerprinting to Enhance Wireless Security using Nonparametric Bayesian Method
Publication TypeConference Paper
Year of Publication2011
AuthorsNguyen, N, Zheng G, Han Z, Zheng R
Conference NameProceedings of the 30th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM)
PublisherIEEE
Conference LocationShanghai, China
Abstract

Each wireless device has its unique fingerprint,which can be utilized to help device identification and malicious detection. Most existing literature employs supervised learning and assumes the number of devices is known. In this paper, from device-dependent channel-invariant radio-metrics, we propose a non-parametric Bayesian method to detect the number of devices as well as classify multiple devices in a unsupervised passive manner. Specifically, the infinite Gaussian mixture model is used and a modified collapsed Gibbs sampling method is proposed. Sybil attack and Masquerade attack are investigated. We have proven the effectiveness of the proposed method by both simulation data and experiment results obtained by USRP2 and Zigbee devices.

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