Device Fingerprinting to Enhance Wireless Security using Nonparametric Bayesian Method

Each wireless device has its unique fingerprint, which can be utilized for device identification and intrusion detection. Most existing literature employs supervised learning techniques and assumes the number of devices is known. In this paper, based on device-dependent channel-invariant radiometries, 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 attacks and Masquerade attacks are investigated. We have proven the effectiveness of the proposed method by both simulation data and experimental measurements obtained by USRP2 and Zigbee devices.