Near Field Communication (NFC) has enabled mobile phones to emulate contactless smart cards. Similar to contactless smart cards, they are also susceptible to relay attacks. To counter these, a number of methods have been proposed that rely primarily on ambient sensors as a proximity detection mechanism (also known as an anti-relay mechanism). In this paper, we, for the first time in academic literature, empirically evaluate a comprehensive set of ambient sensors for their effectiveness as a proximity detection mechanism. We selected 15 out of a total of 17 sensors available via the Google Android platform for evaluation, with the other two sensors unavailable on widely-used handsets. In existing academic literature, only 5 sensors have been proposed with positive results as a potential proximity detection mechanism. Each sensor, where feasible, was used to record the measurements of 1000 contactless transactions at four different physical locations. A total of 252 random users, random sample of the university student population, were involved during the field trails. The analysis of these transactions provides an empirical foundation to categorically answer whether ambient sensors provide a strong proximity detection mechanism for security sensitive applications like banking, transport and high-security access control. After careful analysis, we conclude that no single evaluated mobile ambient sensor is suitable for such critical applications in realistic deployment scenarios. Lastly, we identify a number of potential avenues that may improve their effectiveness.
@cite_1 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 of detecting co-located devices.
@cite_1
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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. 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. 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.