Over the past decade, smartphones have become the point of convergence for many applications and services. There is a growing trend in which traditional smart-card based services like banking, transport and access control are being provisioned through smartphones. Smartphones with Near Field Communication (NFC) capability can emulate a contactless smart card; popular examples of such services include Google Pay and Apple Pay. Similar to contactless smart cards, NFC-based smartphone transactions are susceptible to relay attacks. For contactless smart cards, distance-bounding protocols are proposed to counter such attacks; for NFC-based smartphone transactions, ambient sensors have been proposed as potential countermeasures. In this study, we have empirically evaluated the suitability of ambient sensors as a proximity detection mechanism for contactless transactions. To provide a comprehensive analysis, we also collected relay attack data to ascertain whether ambient sensors are able to thwart such attacks effectively. We initially evaluated 17 sensors before selecting 7 sensors for in-depth analysis based on their effectiveness as potential proximity detection mechanisms within the constraints of a contactless transaction scenario. Each sensor was used to record 1000 legitimate and relay (illegitimate) contactless transactions at four different physical locations. The analysis of these transactions provides an empirical foundation on which to determine whether ambient sensors provide a strong proximity detection mechanism for security-sensitive applications like banking, transport and high-security access control.
@cite_2 demonstrated the suitability of using ambient sound and light for proximity detection. Here, the authors analysed the sensor measurements -- collected for 2 and 30 seconds duration for light and audio respectively -- using a range of similarity comparison algorithms. Extensive experiments were performed in different physical locations, with a high success rate in detecting co-located devices.
@cite_2
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• Using ambient sensors: to measure the characteristics of the surrounding environment, such as light, sound, temperature and GPS. The underlying assumption is that the malicious and honest readers should be in two different locations with different environments. • Problem: All these solutions will not work if the two readers are located in the same environment (e.g., in two nearby stalls in the same mall). In this work, we present TTP as a countermeasure that defends against the reader-and-ghost attack even when the malicious and honest readers are in the same environment. Our solution is based on the following observation: As the result of physical tapping between a pair of sensor-rich devices (say two NFC-enabled phones acting as a card and a reader), the tapping event causes unpredictable but strongly correlated vibrations at both sides, which are hard to reproduce and can be reliably measured by accelerometer sensors. This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area. This paper presents an algorithm for navigating in challenging indoor environments that do not have WiFi. Dead-reckoning (DR) based on off-the-shelf smartphone sensors and magnetic matching (MM) based on indoor magnetic features are integrated. For DR, we utilize a two-filter algorithm structure and multi-level constraints to navigate under different human motion conditions. For MM, we use several approaches to enhance its performance. These approaches include multi-dimensional dynamic time warping, weighted k -nearest neighbor, and utilization of magnetic gradient fingerprints. Furthermore, realized that the key to enhance the DR MM performance is to mitigate the impact of MM mismatches, we introduced and evaluated two mismatch-detection approaches, including a threshold-based method that sets the measurement noises of MM positions based on their distances to the historical DR MM position solutions, and an adaptive Kalman filter-based method that introduces the estimation of the innovation sequence covariance into the calculation of the gain matrix instead of adjusting the measurement noises. The proposed mismatch-detection mechanism reduced the DR MM errors by 45.9 -67.9 in indoor tests with two smartphones, in two buildings, and under four motion conditions. We describe a novel method for symbolic location discovery of simple objects. The method requires no infrastructure and relies on simple sensors routinely used in sensor nodes and smart objects (acceleration, sound). It uses vibration and short, narrow frequency 'beeps' to sample the response of the environment to mechanical stimuli. The method works for specific locations such as 'on the couch', 'in the desk drawer' as well as for location classes such as 'closed wood compartment' or 'open iron surface'. In the latter case, it is capable of generalizing the classification to locations the object has not seen during training. We present the results of an experimental study with a total of over 1200 measurements from 35 specific locations (taken from 3 different rooms) and 12 abstract location classes. It includes such similar locations as the inner and outer pocket of a jacket and a table and shelf made of the same wood. Nonetheless on locations from a single room (16 in the largest one) we achieve a recognition rate of up to 96 . It goes down to 81 if all 35 locations are taken together, however the correct location is in the 3 top picks of the system 94 of the times.