University of Oviedo
The authors investigate whether a classifier can continuously authenticate users based on the way they interact with the touch screen of a smart phone. They propose a set of 30 behavioral touch features that can be extracted from raw touch screen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, they collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. They propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen.