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In this paper we evaluate mobile active authentication based on an ensembleof biometrics and behavior-based profiling signals. We consider seven differentdata channels and their combination. Touch dynamics (touch gestures andkeystroking), accelerometer, gyroscope, WiFi, GPS location and app usage areall collected during human-mobile interaction to authenticate the users. Weevaluate two approaches: one-time authentication and active authentication. Inone-time authentication, we employ the information of all channels availableduring one session. For active authentication we take advantage of mobile userbehavior across multiple sessions by updating a confidence value of theauthentication score. Our experiments are conducted on the semi-uncontrolledUMDAA-02 database. This database comprises smartphone sensor signals acquiredduring natural human-mobile interaction. Our results show that different traitscan be complementary and multimodal systems clearly increase the performancewith accuracies ranging from 82.2% to 97.1% depending on the authenticationscenario.

MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns