User Verification Based on Mouse Dynamics: a Comparison of Public Data Sets

In this paper, we compare the performance of user verification systems based on three publicly available mouse dynamics data sets. One of these datasets is our new DFL data set which contains the mouse dynamics of 21 different users. Two aspects of mouse dynamics based user verification systems are investigated: the effect of the quantity of training data on the performance, and the effect on the performance of the number of consecutive mouse actions used for user identity predictions. Measurements show that it is advisable to use approximately 1000 mouse actions for training (60 minutes of active mouse movements on average), and at least 10 mouse actions for user identity prediction.