2012/07/31 by Mario Frank, Ralf Biedert, Eugene Ma +2 · 2 citations
Computer Science · #cs.CR #cs.LG
paper · pdf · doi:10.1109/tifs.2012.2225048
published as IEEE Transactions on Information Forensics and Security (Vol. 8, No. 1), pages 136-148, 2013 · to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics/
arxiv created 2012/10/08 · arxiv updated 2013/01/08
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 how 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 interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.