Secure Proximity Detection and Verification

Abstract

We live in a world where much of our interactions with the environment around us depend on us being physically close to them. For instance, we have proximity­based tokens (e.g., keys and smartcards) for access systems installed at various places such as in cars, at contactless payment terminals, and in electronic passports. Moreover, such systems exist in critical environments like nuclear power plants.Unfortunately, the current systems used to detect proximity between devices and/or users are rife with vulnerabilities. Numerous attacks, such as Relay attack, Preamble Injection attack, Early Detect/Late Commit, and Cicada, exist that let an attacker maliciously alter the measured distance. The research community has proposed several solutions to address these problems and based on their inputs, the IEEE 802.15.4a standard was recently amended. Nevertheless, we show that the newer amendment(i.e., IEEE 802.15.4z) is however not entirely secure and still vulnerable to being exploited.In this work, we evaluate and address the vulnerabilities present in the recently introduced standard,IEEE 802.15.4z amendment for Ultra­Wide Band (UWB). This standard forms the basis of proximity detection in a majority of new devices such as keyfobs for cars, access control systems, smartphones like Samsung S21 and Google Pixel 6, and even medical equipment to monitor patients. First, we mount two attacks, namely the Cicada­TF and the Adaptive Injection, against UWB ­based proximity detection systems. Second, we propose a novel approach to detect the presence of these attacks. We create a real ­world testbed using DWM3000 ICs mounted on NRF52840­devkits to launch the attacks and implement our proposed detection approach. We evaluate the efficacy of our approach in three different environments: an indoor residence, a large outdoor passageway, and an office space. These environments were selected to represent the most commonly used places and were based on the802.15.4a channel models document by IEEE. Our experiment results show that the proposed model can detect the presence of attacks with high accuracy (94%) in all three environments. To the best of our knowledge, this is the first research work that presents a way to detect the presence of such attacks and also to be verified on hardware.