MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns
In this paper we evaluate how discriminative are behavior-based signals obtained from the smartphone sensors. The main aim is to evaluate these signals for person recognition. The recognition based on these signals increases the security of devices, but also implies privacy concerns. We consider seven different data channels and their combinations. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage of mobile user behavior across multiple sessions by updating a confidence value of the authentication score. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises of smartphone sensor signals acquired during natural human-mobile interaction. Our results show that different traits can be complementary and multimodal systems clearly increase the performance with accuracies ranging from 82.2% to 97.1% depending on the authentication scenario. These results confirm the discriminative power of these signals.