X-Mode: A Real Time Approach of Discriminating Wi-Fi Network Impersonators

Current Wi-Fi network infrastructure inherently lacks reliable positional knowledge of the origin of individual network packets. As a consequence, attackers are potentially able to impersonate legitimate Wi-Fi network nodes, including both clients and access points, by generating network traffic with all the characteristics of legitimate traffic. To exclusively discriminate illegitimate nodes, we propose a method of using multiple and multiple-angle measurements of a Wi-Fi network nodepsilas signal strength. In this paper we present a real time unsupervised analysis method for identifying duplicate Wi-Fi network nodes which are physically separate. We demonstrate that, given a dense sensor network, data from a small set of identifiable critical sensors are much more valuable in node discrimination than those from other sensors, enabling large improvement in responding speed and discrimination accuracy.