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Retraining a Novelty Detector with Impostor Patterns for Keystroke Dynamics-Based Authentication

  • Hyoung-joo Lee
  • Sungzoon Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

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.

Keywords

Support Vector Machine Normal Pattern Novelty Detector False Acceptance Rate False Rejection Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hyoung-joo Lee
    • 1
  • Sungzoon Cho
    • 1
  1. 1.Department of Industrial EngineeringSeoul National UniversitySeoulKorea

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