  In this paper we evaluate mobile active authentication based on an ensemble
of biometrics and behavior-based profiling signals. We consider seven different
data channels and their combination. 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 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.
