Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

• Main Authors: Frank, Mario, Biedert, Ralf, Ma, Eugene, Martinovic, Ivan, Song, Dawn
• Format: Journal Article
• Language: English
• Published: 08-10-2012
• Subjects: Computer Science - Cryptography and Security; Computer Science - Learning
• DOI: 10.48550/arxiv.1207.6231

IEEE Transactions on Information Forensics and Security (Vol. 8,
No. 1), pages 136-148, 2013 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.