User re-authentication via mouse movements

We present an approach to user re-authentication based on the data collected from the computer's mouse device. Our underlying hypothesis is that one can successfully model user behavior on the basis of user-invoked mouse movements. Our implemented system raises an alarm when the current behavior of user X, deviates sufficiently from learned "normal" behavior of user X. We apply a supervised learning method to discriminate among k users. Our empirical results for eleven users show that we can differentiate these individuals based on their mouse movement behavior with a false positive rate of 0.43% and a false negative rate of 1.75%. Nevertheless, we point out that analyzing mouse movements alone is not sufficient for a stand-alone user re-authentication system.