Abstract
Password is the most widely used identity verification method in computer security domain. However, due to its simplicity, it is vulnerable to imposter attacks. Keystroke dynamics adds a shield to password. Discriminating imposters from owners is a novelty detection problem. Recent research reported good performance of Auto-Associative Multilayer Perceptron(AaMLP). However, the 2-layer AaMLP cannot identify nonlinear boundaries, which can result in serious problems in computer security. In this paper, we applied 4-layer AaMLP as well as SVM as novelty detector to keystroke dynamics identity verification, and found that they can significantly improve the performance.
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© 2003 Springer-Verlag Berlin Heidelberg
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Yu, E., Cho, S. (2003). Novelty Detection Approach for Keystroke Dynamics Identity Verification. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_143
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DOI: https://doi.org/10.1007/978-3-540-45080-1_143
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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