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 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.