A data clustering approach to discriminating impersonating devices in Wi-Fi networks

Abstract

By design, Wi-Fi networks inherently lack reliable positional knowledge of the origin of individual network packets, and instead use the media access control (MAC) address of a received packet to assign packet identity. Given that there are a variety of tools that allow one to construct arbitrary network packets and transmit over a network, a potential security risk arises in that it is difficult to discriminate legitimate packets from packets generated by an intruder solely on the basis of packet content. We describe a data clustering approach to discriminating network nodes based on signal strength data measured from multiple angles through a distributed sensor network, and demonstrate tha..