IEEE Internet of Things Journal
1 Dec 2016
We develop a new location spoofing detectionalgorithm for geo-spatial tagging and location-based servicesin the Internet of Things (IoT), called Enhanced LocationSpoofing Detection using Audibility (ELSA) which can beimplemented at the backend server without modifying existinglegacy IoT systems. ELSA is based on a statistical decisiontheory framework and uses two-way time-of-arrival (TW-TOA)information between the user's device and the anchors. Inaddition to the TW-TOA information, ELSA exploits theimplicitly available audibility information to improve detectionrates of location spoofing attacks. Given TW-TOA and audibilityinformation, we derive the decision rule for the verification of thetag's location, based on the generalized likelihood ratio test. Wedevelop a practical threat model for delay measurements spoofingscenarios, and investigate in detail the performance of ELSA interms of detection and false alarm rates. Our extensive simulationresults on both synthetic and real-world datasets demonstratethe superior performances of ELSA compared to conventionalnon-audibility-aware approaches.