User-Specific Feature Selection in Keystroke Dynamics Authentication

Keystroke dynamics is a popular behavioral biometric for user authentication. This paper explores the potential of user-specific feature selection in performance improvement beyond existing template adaptation strategies. Four experiments with CMU and GreyC datasets evaluated the approach. Two impostor sample selection methods were tested: the head method versus random sampling. Experiments 1 and 2 used the head method for the CMU and GreyC datasets, while Experiments 3 and 4 employed random sampling. GreyC had shown better EER improvement. The CMU dataset achieved a better improvement using random sampling, while the GreyC dataset did the same with the head method, likely due to differences in password characteristics and familiarity.