  We develop a new location spoofing detection algorithm for geo-spatial
tagging and location-based services in the Internet of Things (IoT), called
Enhanced Location Spoofing Detection using Audibility (ELSA) which can be
implemented at the backend server without modifying existing legacy IoT
systems. ELSA is based on a statistical decision theory framework and uses
two-way time-of-arrival (TW-TOA) information between the user's device and the
anchors. In addition to the TW-TOA information, ELSA exploits the implicit
available audibility information to improve detection rates of location
spoofing attacks. Given TW-TOA and audibility information, we derive the
decision rule for the verification of the device's location, based on the
generalized likelihood ratio test. We develop a practical threat model for
delay measurements spoofing scenarios, and investigate in detail the
performance of ELSA in terms of detection and false alarm rates. Our extensive
simulation results on both synthetic and real-world datasets demonstrate the
superior performance of ELSA compared to conventional non-audibility-aware
approaches.

Cryptography and Security