An Indirect Authentication Scheme for Mobile Users Based on User’s Behavior Model

The current authentication methods are vulnerable to threats and significantly more frustrating, leading users to create and reuse shorter passwords and pins, or no authentication at all. Research in implicit authentication suggests that the time period a specific trait is monitored is useful in identification of user behavior. In order to improve the performance of authentication quality, we have described an automated approach to generate user’s behavior models and to differentiate between normal usage and abnormal usage. We have discussed on how to efficiently authenticate users implicitly on mobile devices. To validate the performance of our approach, we used data collected from 30 users of mobile devices and the approach recorded high accuracy rate. The result shows that it can improve the performance in the authentication substantially.