Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication Mario Frank, Ralf Biedert, Eugene Ma, Ivan Martinovic, Dawn Song We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test if this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We 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 only a small amount of touch data.The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% for long-term authentication where the authentication test was one week after the enrollment phase.