A method for improving mobile authentication using human spatio-temporal behavior

Integration of NFC radios into smartphones is expediting the adoption of mobile devices as the preferred method for accessing physical locations, bank accounts, and other valuable resources. The pervasive nature of authentication using these mobile devices, however, comes with increased security considerations stemming from the possibility of physical loss of the device. To minimize the risk caused by stolen devices, this paper introduces a method for confirming the identity of a device's user based on her recent macroscopic behavior over space and time. The user's behavior is continuously recorded by a set of devices embedded in the environment (e.g., Wi-Fi Access Point) and used to train a probabilistic n-gram model. Subsequently, deviations caused by stolen devices can be detected by comparing the user's recent behavior against the trained model. Our first evaluation results demonstrate the ability of the proposed approach to detect anomalies in the user's behavior without generating a significant number of false alarms.