Geo-spatial Location Spoofing Detection for Internet of Things

TL;DR

This paper introduces ELSA, a novel backend algorithm for detecting location spoofing in IoT systems using TW-TOA and audibility data, significantly improving detection accuracy without system modifications.

Contribution

The paper presents ELSA, a new audibility-aware detection algorithm based on statistical decision theory that enhances spoofing detection in IoT without altering existing infrastructure.

Findings

ELSA outperforms conventional methods in detection accuracy.

ELSA achieves low false alarm rates in diverse scenarios.

Performance validated on synthetic and real-world datasets.

Abstract

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.