Adaptive Positive Selection for Keystroke Dynamics

Current technologies provide state of the art services but, at the same time, increase data exposure, mainly due to Internet-based applications. In view of this scenario, improved authentication mechanisms are needed. Keystroke dynamics, which recognizes users by their typing rhythm, is a cost-effective alternative. This technology usually only requires a common keyboard in order to acquire authentication data. There are several studies investigating the use of machine learning techniques for user authentication based on keystroke dynamics. However, the majority of them assume a scenario which the user model is not updated. It is known that typing rhythm changes over time (concept drift). Consequently, classification algorithms in keystroke dynamics have to be able to adapt the user model to these changes. This paper evaluates adaptation methods for an immune positive selection algorithm in a data stream context. Experimental results showed that they improved classification performance, mainly for false rejection rates.