A Behavior-Based Proactive User Authentication Model Utilizing Mobile Application Usage Patterns.

Access to smart home networks is mostly achieved through end-user devices, especially mobile phones, but such devices are susceptible to theft or loss. In this paper, we present a user authentication model based on application access events, using only a small amount of information, thus reducing the computation time. To validate our model, we utilize a public real-world dataset collected from real users over a long period of time, in an uncontrolled manner. The model is evaluated by differentiating between users who utilize shared apps at the same daily intervals. In addition, we evaluate various classification approaches regarding legitimate user classification in compliance with the history of app usage events. The results demonstrate the capacity of the presented model to authenticate users with high true positive and true negative rates.