User identification via keystroke characteristics of typed names using neural networks
International Journal of Man-Machine Studies
Typing Patterns: A Key to User Identification
IEEE Security and Privacy
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Verification of computer users using keystroke dynamics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Why did my detector do that?!: predicting keystroke-dynamics error rates
RAID'10 Proceedings of the 13th international conference on Recent advances in intrusion detection
Expert Systems with Applications: An International Journal
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Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier.