Location Verification Systems Based on Received Signal Strength With Unknown Transmit Power

—In the context of location verification systems (LVSs), this work proves that knowledge of a legitimate user’s transmit power has no effect on the optimal performance of an RSS-based LVS. Specifically, we prove that the detection performance of a generalized likelihood ratio test (GLRT), where the unknown transmit power is estimated, is identical to that of a differential likelihood ratio test (D-LRT). Our analysis also proves the asymptotic optimality of D-LRT for an RSS-based LVS with unknown transmit power. These results are important for realworld deployments of LVSs, since D-LRT incurs a significantly lower implementation cost relative to GLRT.ARC Discovery Projects Grant DP150103905

Optimal Information-Theoretic Wireless Location Verification

We develop a new Location Verification System (LVS) focussed on network-based Intelligent Transport Systems and vehicular ad hoc networks. The algorithm we develop is based on an information-theoretic framework which uses the received signal strength (RSS) from a network of base-stations and the claimed position. Based on this information we derive the optimal decision regarding the verification of the user's location. Our algorithm is optimal in the sense of maximizing the mutual information between its input and output data. Our approach is based on the practical scenario in which a non-colluding malicious user some distance from a highway optimally boosts his transmit power in an attempt to fool the LVS that he is on the highway. We develop a practical threat model for this attack scenario, and investigate in detail the performance of the LVS in terms of its input/output mutual information. We show how our LVS decision rule can be implemented straightforwardly with a performance that delivers near-optimality under realistic threat conditions, with information-theoretic optimality approached as the malicious user moves further from the highway. The practical advantages our new information-theoretic scheme delivers relative to more traditional Bayesian verification frameworks are discussed.Comment: Corrected typos and introduced new threat model

Location Verification Systems Under Spatially Correlated Shadowing

The verification of the location information utilized in wireless communication networks is a subject of growing importance. In this work we formally analyze, for the first time, the performance of a wireless Location Verification System (LVS) under the realistic setting of spatially correlated shadowing. Our analysis illustrates that anticipated levels of correlated shadowing can lead to a dramatic performance improvement of a Received Signal Strength (RSS)-based LVS. We also analyze the performance of an LVS that utilizes Differential Received Signal Strength (DRSS), formally proving the rather counter-intuitive result that a DRSS-based LVS has identical performance to that of an RSS-based LVS, for all levels of correlated shadowing. Even more surprisingly, the identical performance of RSS and DRSS-based LVSs is found to hold even when the adversary does not optimize his true location. Only in the case where the adversary does not optimize all variables under her control, do we find the performance of an RSS-based LVS to be better than a DRSS-based LVS. The results reported here are important for a wide range of emerging wireless communication applications whose proper functioning depends on the authenticity of the location information reported by a transceiver.ARC Discovery Projects Grant DP150103905

An Information Theoretic Location Verification System for Wireless Networks

As location-based applications become ubiquitous in emerging wireless networks, Location Verification Systems (LVS) are of growing importance. In this paper we propose, for the first time, a rigorous information-theoretic framework for an LVS. The theoretical framework we develop illustrates how the threshold used in the detection of a spoofed location can be optimized in terms of the mutual information between the input and output data of the LVS. In order to verify the legitimacy of our analytical framework we have carried out detailed numerical simulations. Our simulations mimic the practical scenario where a system deployed using our framework must make a binary Yes/No "malicious decision" to each snapshot of the signal strength values obtained by base stations. The comparison between simulation and analysis shows excellent agreement. Our optimized LVS framework provides a defence against location spoofing attacks in emerging wireless networks such as those envisioned for Intelligent Transport Systems, where verification of location information is of paramount importance