On Passive Wireless Device Fingerprinting using Infinite Hidden Markov Random Field

This paper presents a new concept of device fingerprinting (or profiling) to enhance wireless security using Infinite Hidden Markov Random Field (iHMRF). Wireless device fingerprinting is an emerging approach for detecting spoofing attacks in wireless network. Existing methods utilize either time-independent features or time-dependent features, but not both concurrently due to the complexity of different dynamic patterns. In this paper, we present a unified approach to fingerprinting based on iHMRF. The proposed approach is able to model both time-independent and time-dependent features, and to automatically detect the number of devices that is dynamically varying. We propose the first iHMRF-based online classification algorithm for wireless environment using variational incremental inference, micro-clustering techniques, and batch updates. Extensive simulation evaluations demonstrate the effectiveness and efficiency of this new approach.