Advertisement

Biometric Access Control Through Numerical Keyboards Based on Keystroke Dynamics

  • Ricardo N. Rodrigues
  • Glauco F. G. Yared
  • Carlos R. do N. Costa
  • João B. T. Yabu-Uti
  • Fábio Violaro
  • Lee Luan Ling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

This paper presents a new approach for biometric authentication based on keystroke dynamics through numerical keyboards. The input signal is generated in real time when the user enters with target string. Five features were extracted from this input signal (ASCII key code and four keystroke latencies) and four experiments using samples for genuine and impostor users were performed using two pattern classification technics. The best results were achieved by the HMM (EER=3.6%). This new approach brings security improvements to the process of user authentication, as well as it allows to include biometric authentication in mobile devices, such as cell phones.

Keywords

Hide Markov Model User Authentication Biometric System Biometric Authentication Biometric Feature 
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.

References

  1. 1.
    Monrose, F., Rubin, A.D.: Keystroke Dynamics as a Biometric for Authentication. Future Generation Computer Systems 16(4), 351–359 (1999)CrossRefGoogle Scholar
  2. 2.
    Joyce, R., Gupta, G.: Identity authentication based on keystroke latencies. commun. 33(2), 168–176 (1990)CrossRefGoogle Scholar
  3. 3.
    Bleha, D., Obaidat, M.: Dimensionality reduction and feature extraction applications in identifying computer users. IEEE Trans. Syst., Man, Cybern. 21(2), 452–456 (1991)CrossRefGoogle Scholar
  4. 4.
    Lin, D.T.: Computer-access authentication with neural network based keystroke identity verification. In: Proc. Int. Conf. Neural Networks, vol. 1, pp. 174–178 (1997)Google Scholar
  5. 5.
    Obaidat, M.S., Sadoun, B.: Verification of computer user using keystroke dynamics. IEEE Trans. Syst., Man, Cybern. 27(2), 261–269 (1997)CrossRefGoogle Scholar
  6. 6.
    Monrose, F., Reiter, M.K., Wetzel, S.: Password hardening based on keystroke dynamics. In: Proc. 6th ACM Conf. Computer Security, Singapore (November 1999)Google Scholar
  7. 7.
    Wong, F.W.M.H., Supian, A.S.M., Ismail, A.F., Kin, L.W., Soon, O.C.: Enhanced user authentication through typing biometrics with artificial neural network and k-nearest neighbor algorithm. In: Conf. Rec. 35th Asilomar Conf. Signals, Syst., comput., vol. 2, pp. 911–915 (2001)Google Scholar
  8. 8.
    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 Trans. on Signal Processing 53(2) (February 2005)Google Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn., October 2000. Wiley-Interscience Publication, Hoboken (2000)Google Scholar
  10. 10.
    Cambridge University Engineering Departament, The HTK Book. Cambridge University, Cambridge (2002)Google Scholar
  11. 11.
    Ord, T., Furnell, S.M.: User authentication for keypad-based devices using keystroke analysis. In: Proc. Second International Network Conference (INC 2000), Plymouth, UK, pp. 263–272 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ricardo N. Rodrigues
    • 1
  • Glauco F. G. Yared
    • 1
  • Carlos R. do N. Costa
    • 1
  • João B. T. Yabu-Uti
    • 1
  • Fábio Violaro
    • 1
  • Lee Luan Ling
    • 1
  1. 1.Laboratory of Pattern Recognition and Computer Networks, Department of Communications, School of Electrical and Computer EngineeringState University of CampinasCampinasBrazil

Personalised recommendations