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)


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.


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.


  1. 1.
    Gaines, R., Lisowski, W., Press, S., Shapiro, N.: Authentication by keystroke timing: some preliminary results. Rand Report R-256-NSF. Rand Corporation (1980)Google Scholar
  2. 2.
    Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer, Norwell (1999)Google Scholar
  3. 3.
    Monrose, F., Rubin, A.D.: Keystroke Dynamics as a Biometric for Authentication. Future Generation Computer System 16(4), 351–359 (2000)CrossRefGoogle Scholar
  4. 4.
    Araújo, L.C.F., Sucupira Jr., L.H.R., Lizárraga, M.G., Ling, L.L., Yabu-Uti, J.B.T.: User Authentication through Typing Biometrics Features. IEEE Transactions on Signal Processing 52(2), 851–855 (2005)CrossRefGoogle Scholar
  5. 5.
    Bleha, S., Slivinsky, C., Jussein, B.: Computer-access Security Systems using Keystroke Dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(12), 1217–1222 (1990)CrossRefGoogle Scholar
  6. 6.
    Obaidat, M.S., Sadoun, B.: Verification of Computer Users using Keystroke Dynamics. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 27(2), 261–269 (1997)CrossRefGoogle Scholar
  7. 7.
    Cho, S., Han, C., Han, D., Kim, H.: Web Based Keystroke Dynamics Identity Verification using Neural Networks. Journal of Organizational Computing and Electronic Commerce 10(4), 295–307 (2000)CrossRefGoogle Scholar
  8. 8.
    Yu, E., Cho, S.: Keystroke Dynamics Identity Verification - Its Problems and Practical Solutions. Computer and Security 23(5), 428–440 (2004)CrossRefGoogle Scholar
  9. 9.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the Support of a High-dimensional Distribution. Neural Computation 13, 1443–1471 (2001)zbMATHCrossRefGoogle Scholar
  10. 10.
    Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Machine Learning 54, 45–66 (2004)zbMATHCrossRefGoogle Scholar
  11. 11.
    Lee, H., Cho, S.: SOM-based Novelty Detection Using Novel Data. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 359–366. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Golfarelli, M., Maio, D., Maltoni, D.: On the Error-Reject Trade-off in Biometric Verification Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 786–796 (1997)CrossRefGoogle Scholar

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

Personalised recommendations