Secure Location of Things (SLOT): Mitigating Localization Spoofing Attacks in the Internet of Things

The rise of geo-spatial location-based applications for the Internet of Things introduces new location spoofing security risks. To overcome the threat of malicious spoofing attacks, we develop the Secure Location of Things (SLOT) framework which extends current state-of-the-art methods and is able t...

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Bibliographic Details
Published in:IEEE internet of things journal 2017-12, Vol.4 (6), p.2199-2206
Main Authors: Pengfei Zhang, Nagarajan, Sai Ganesh, Nevat, Ido
Format: Article
Language:English
Subjects:
Algorithms
Byzantine spoofing attack
Computer simulation
Cybersecurity
Delays
Distance measurement
Estimating techniques
Internet of Things
Internet of Things (IoT)
localization
Maximum likelihood estimators
Protocols
sensor networks
Spoofing
State of the art
Wireless communication
Wireless sensor networks
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Summary:The rise of geo-spatial location-based applications for the Internet of Things introduces new location spoofing security risks. To overcome the threat of malicious spoofing attacks, we develop the Secure Location of Things (SLOT) framework which extends current state-of-the-art methods and is able to cope with such threats. The SLOT framework incorporates another piece of information which has not been utilized so far, the audibility information. This information indicates whether a node is able/unable to communicate with the target. By leveraging on this available information, we reformulate the location estimation problem as a stochastic censoring model and derive the maximum likelihood estimator for the node's location in two different ways: the first algorithm is based on a probabilistic mixture model and assumes knowledge of the Byzantine attack distributional model; and the second algorithm is based on a difference-timeof-arrival, which does not make any distributional assumptions regarding the attack. We show that our algorithms provide significant performance gain over current state-of-the-art algorithms, by mitigating the well-known ambiguity problem of the likelihood surface. Extensive simulations show the significant benefits that the SLOT framework provides compared to current state-of-the-art algorithms.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2017.2753579