A data clustering approach to discriminating impersonating devices in Wi-Fi networks
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 that relatively little network data are sufficient for high discrimination accuracy. Copyright (C) 2009 John Wiley Sons, Ltd.
wi-fi networks security impersonation detection