Keystroke Dynamics in a General Setting

  • Rajkumar Janakiraman
  • Terence Sim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


It is well known that Keystroke Dynamics can be used as a biometric to authenticate users. But most work to date use fixed strings, such as userid or password. In this paper, we study the feasibility of using Keystroke Dynamics as a biometric in a more general setting, where users go about their normal daily activities of emailing, web surfing, and so on. We design two classifiers that appropriate for one-time and continuous authentication. We also propose a new Goodness Measure to compute the quality of a word used for Keystroke Dynamics. From our experiments we find that, surprisingly, non-English words are better suited for identification than English words.


Keystroke dynamics biometrics 


  1. 1.
    Shepherd, S.J.: Continuous authentication by analysis of keyboard typing characteristics. In: IEEE Conf. on Security and Detection, European Convention, pp. 111–114. IEEE Computer Society Press, Los Alamitos (1995)CrossRefGoogle Scholar
  2. 2.
    Fry, E.B., Kress, J.E., Fountoukidis, D.L.: The Reading Teachers Book of Lists, 3rd edn.Google Scholar
  3. 3.
    Monrose, F., Reiter, M.K., Wetzel, S.: Password hardening based on keystroke dynamics. In: Proceedings of the 6th ACM Conference on Computer and Communications Security, ACM Press, New York (1999)Google Scholar
  4. 4.
    Rodrigues, R.N., Yared, G.F.G., Costa, C.R.D., Yabu Uti, J.B.T., Violaro, F., Ling, L.L.: Biometric Access Control Through Numerical Keyboards Based on Keystroke Dynamics. In: International Conference of Biometrics, pp. 640–646 (2006)Google Scholar
  5. 5.
    Kumar, S., Sim, T., Janakiraman, R., Zhang, S.: Using Continuous Biometric Verification to Protect Interactive Login Sessions. In: Srikanthan, T., Xue, J., Chang, C.-H. (eds.) ACSAC 2005. LNCS, vol. 3740, pp. 441–450. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Joyce, R., Gupta, G.: Identity authentication based on keystroke latencies. Communications of the ACM 33(2), 168–176 (1990)CrossRefGoogle Scholar
  7. 7.
    Villani, M., Tappert, C., Ngo, G., Simone, J., St. Fort, H., Cha, S.: Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p. 39. IEEE Computer Society, Washington (2006)CrossRefGoogle Scholar
  8. 8.
    Rao, B.: Continuous Keystroke Biometric System M.S. thesis, Media Arts and Technology, UCSB (2005)Google Scholar
  9. 9.
    Leggett, J., Williams, G., Usnick, M., Longnecker, M.: Dynamic identity verification via keystroke characteristics. Int. J. Man-Mach. Stud. 35(6), 859–870 (1991)CrossRefGoogle Scholar
  10. 10.
    Obaidat, M.S., Sadoun, B.: Keystroke Dynamics Based Authentication. Ch. 10, Textbook (1998),
  11. 11.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley and Sons, Chichester (2000)Google Scholar
  12. 12.
    Gunetti, D., Picardi, C.: Keystroke analysis of free text. ACM Transactions Information Systems Security 8(3), 312–347 (2005)CrossRefGoogle Scholar
  13. 13.
    Jain, A.K.: Biometric recognition: how do I know who you are? In: Proceedings of the 12th IEEE Signal Processing and Communications Applications Conference, IEEE Computer Society Press, Los Alamitos (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rajkumar Janakiraman
    • 1
  • Terence Sim
    • 1
  1. 1.School of Computing, National University of Singapore,117543Singapore

Personalised recommendations