Location Spoofing Attack Detection in Wireless Networks

While significant research effort has been dedicated to wireless localization over the past decades, most aspects of location security have been overlooked. In particular, adversaries can take advantage of security vulnerabilities of current location systems to launch location spoofing attacks, thus disguising their position in the network. In this paper, we address issues associated with location spoofing attack detection by examining relative location error rather than its absolute value. Specifically, we propose novel statistical and pattern matching techniques called relative error detection (RED) and topological residual fingerprint matching (TRFM) for detecting both signal strength and beamforming attacks. Also, the concept of geometric filtering is developed to considerably improve location reliability by exploiting the geometry of nodes.