Identity verification through dynamic keystroke analysis

Abstract

Typing rhythms are the rawest form of data stemming from the interaction between users and computers. When properly sampled and analyzed, they may become a useful tool to ascertain personal identity. Moreover, unlike other biometric features, typing dynamics have an important characteristic: they still exist and are available even after an access control phase has been passed. As a consequence, keystroke analysis can be used as a viable tool for user authentication throughout the work session. In this paper we present an original approach to identity verification based on the analysis of the typing rhythms of individuals on different texts. Our experiments involve 130 volunteers and reach the best outcomes found in the literature, using a smaller amount of information than in other works, and avoiding any form of tailoring of the system to the available data set. The method described in the paper is easily tuned to reach an acceptable trade-off between the need to spot most impostors and to avoid false alarms, and, as a consequence, it can become a valid aid to intrusion detection.