  In order to protect user privacy on mobile devices, an event-driven implicit
authentication scheme is proposed in this paper. Several methods of utilizing
the scheme for recognizing legitimate user behavior are investigated. The
investigated methods compute an aggregate score and a threshold in real-time to
determine the trust level of the current user using real data derived from user
interaction with the device. The proposed scheme is designed to: operate
completely in the background, require minimal training period, enable high user
recognition rate for implicit authentication, and prompt detection of abnormal
activity that can be used to trigger explicitly authenticated access control.
In this paper, we investigate threshold computation through standard deviation
and EWMA (exponentially weighted moving average) based algorithms. The result
of extensive experiments on user data collected over a period of several weeks
from an Android phone indicates that our proposed approach is feasible and
effective for lightweight real-time implicit authentication on mobile
smartphones.
