Geo-Spatial Location Spoofing Detection for Internet of Things

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Geo-Spatial Location Spoofing Detection for Internet of Things
Title:
Geo-Spatial Location Spoofing Detection for Internet of Things
Journal Title:
IEEE Internet of Things Journal
Keywords:
Publication Date:
01 December 2016
Citation:
J. Y. Koh, I. Nevat, D. Leong and W. C. Wong, "Geo-Spatial Location Spoofing Detection for Internet of Things," in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 971-978, Dec. 2016. doi: 10.1109/JIOT.2016.2535165
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 audibility information (or outage 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.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
2327-4662
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