Computer-Access Security Systems Using Keystroke Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Error-Reject Trade-Off in Biometric Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
SOM-based novelty detection using novel data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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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In keystroke dynamics-based authentication, novelty detection methods have been used since only the valid user’s patterns are available when a classifier is built. After a while, however, impostors’ keystroke patterns become also available from failed login attempts. We propose to retrain the novelty detector with the impostor patterns to enhance the performance. In this paper the support vector data description (SVDD) and the one-class learning vector quantization (1-LVQ) are retrained with the impostor patterns. Experiments on 21 keystroke pattern datasets show that the performance improves after retraining and that the one-class learning vector quantization outperforms other widely used novelty detectors.