Adaptive approaches for keystroke dynamics

Enhanced authentication mechanisms are currently needed in several situations. Mainly due to the widespread use of the Internet, data exposure became a source of growing concern. Commonly used login and password credentials may not provide enough security in this scenario, as they may be easily stolen or guessed in some cases. The use of biometrics is a prominent alternative for user authentication, such as by the use of keystroke dynamics. This biometric technology allows the recognition of users by their typing rhythm, which can be performed using data provided by a common keyboard. However, recent work has shown that typing rhythm changes over time. As a result, a static biometric model can become outdated, decreasing the predictive performance of the system. In light of this fact, there is a need for new techniques able to dynamically adapt user models over time. This paper evaluates, in a data stream context, algorithms proposed in the literature for user authentication based on keystroke dynamics. Modifications to these algorithms are also proposed and evaluated. A study of the behaviour of the algorithms over time under several aspects is also performed. According to our experiments, adaptive methods can improve predictive performance of user recognition by keystroke dynamics.