Dynamic identity verification via keystroke characteristics
International Journal of Man-Machine Studies
User identification via keystroke characteristics of typed names using neural networks
International Journal of Man-Machine Studies
A methodology for improving computer access security
Computers and Security
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
User authentication through keystroke dynamics
ACM Transactions on Information and System Security (TISSEC)
Support Vector Data Description
Machine Learning
Typing Patterns: A Key to User Identification
IEEE Security and Privacy
User authentication through typing biometrics features
IEEE Transactions on Signal Processing
Verification of computer users using keystroke dynamics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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User authentication via keystroke dynamics remains a challenging problem due to the fact that keystroke dynamics pattern cannot be maintained stable over time. This paper describes a novel keystroke dynamics-based user authentication approach. The proposed approach consists of two stages, a training stage and an authentication stage. In the training stage, a set of orthogonal bases and a common feature vector are periodically generated from keystroke features of a legitimate user@?s several recent successful authentications. In the authentication stage, the current keystroke feature vector is projected onto the set of orthogonal bases, and the distortion of the feature vector between its projection is obtained. User authentication is implemented by comparing the slope correlation degree of the distortion between the common feature vector with a threshold determined periodically using the recent impostor patterns. Theoretical and experimental results show that the proposed method presents high tolerance to instability of user keystroke patterns and yields better performance in terms of false acceptance rate (FAR) and false rejection rate (FRR) compared with some recent methods.