On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks

Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints

Proximity Assurances Based on Natural and Artificial Ambient Environments

Relay attacks are passive man-in-the-middle attacks that aim to extend the physical distance of devices involved in a transaction beyond their operating environment. In the field of smart cards, distance bounding protocols have been proposed in order to counter relay attacks. For smartphones, meanwhile, the natural ambient environment surrounding the devices has been proposed as a potential Proximity and Relay-Attack Detection (PRAD) mechanism. These proposals, however, are not compliant with industry-imposed constraints that stipulate maximum transaction completion times, e.g. 500 ms for EMV contactless transactions. We evaluated the effectiveness of 17 ambient sensors that are widely-available in modern smartphones as a PRAD method for time-restricted contactless transactions. In our work, both similarity- and machine learning-based analyses demonstrated limited effectiveness of natural ambient sensing as a PRAD mechanism under the operating requirements for proximity and transaction duration specified by EMV and ITSO. To address this, we propose the generation of an Artificial Ambient Environment (AAE) as a robust alternative for an effective PRAD. The use of infrared light as a potential PRAD mechanism is evaluated, and our results indicate a high success rate while remaining compliant with industry requirements