  Implicit authentication (IA) is gaining popularity over recent years due to
its use of user behavior as the main input, relieving users from explicit
actions such as remembering and entering passwords. However, such convenience
comes with a cost of authentication accuracy and delay which we propose to
improve in this paper. Authentication accuracy deteriorates as users' behaviors
change as a result of mood, age, a change of routine, etc. Current
authentication systems handle failed authentication attempts by locking the
users out of their mobile devices. It is unsuitable for IA whose accuracy
deterioration induces a high false reject rate, rendering the IA system
unusable. Furthermore, existing IA systems leverage computationally expensive
machine learning, which can introduce a large authentication delay. It is
challenging to improve the authentication accuracy of these systems without
sacrificing authentication delay. In this paper, we propose a multi-level
privilege control (MPC) scheme that dynamically adjusts users' access privilege
based on their behavior change. MPC increases the system's confidence in users'
legitimacy even when their behaviors deviate from historical data, thus
improving authentication accuracy. It is a lightweight feature added to the
existing IA schemes that helps avoid frequent and expensive retraining of
machine learning models, thus improving authentication delay. We demonstrate
that MPC increases authentication accuracy by 18.63\% and reduces
authentication delay by 7.02 minutes on average, using a public dataset that
contains comprehensive user behavior data.
