Artificial Rhythms and Cues for Keystroke Dynamics Based Authentication

  • Sungzoon Cho
  • Seongseob Hwang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Biometrics based user authentication involves collecting user’s patterns and then using them to determine if a new pattern is similar enough. The quality of the user’s patterns is as important as the quality of the classifier. But, the issue has been ignored in the literature since the popular biometrics are mostly trait based such as finger prints and iris so that its pattern quality depends on the quality of the input device involved. However, the quality of the user’s patterns of behavior based biometric such as keystroke dynamics can be improved artificially by increasing the peculiarity of the typing style. In this paper, we propose several ways to improve the quality. But, first we define the quality of patterns in terms of two factors: uniqueness and consistency. Finally, the results of a preliminary experiment are presented that support the utility of the proposed methods.


Input Device Proper Threshold Valid User Slow Tempo Natural Rhythm 
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.
    Leggett, J., Williams, G., Usnick, M., Longnecker, M.: Dynamic identity verification via keystroke characteristics. Int. J. Man-Machine Studies 35, 859–870 (1991)CrossRefGoogle Scholar
  3. 3.
    Brown, M., Rogers, S.J.: User identification via keystroke characteristics of typed names using neural networks. Int. J. Man-Machine Studies 39, 999–1014 (1993)CrossRefGoogle Scholar
  4. 4.
    Obaidat, M., Sadoun, S.: Verification of computer users using keystroke dynamics. IEEE Transactions on Systems, Man and Cybernetics, Part B:P Cybernetics 27(2), 261–269 (1997)CrossRefGoogle Scholar
  5. 5.
    Cho, S., Han, C., Han, D., Kim, H.: Web-based keystroke dynamics identity verification using neural network. J. Organizational computing and electronic commerce 10(4), 295–307 (2000)CrossRefGoogle Scholar
  6. 6.
    Cho, S., Han, D.: Apparatus for Authenticating an Individual Based on a Typing Pattern by Using a Neural Network System. Patent No. 6,151,593, November 21, US Patent and Trademark Office, Washington DC 20231 (2000)Google Scholar
  7. 7.
    Yu, E., Cho, S.: Keystroke dynamics identity verification - its problems and practical solutions. Computers and Security 23(5), 428–440 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sungzoon Cho
    • 1
  • Seongseob Hwang
    • 1
  1. 1.Department of Industrial EngineeringSeoul National UniversitySeoulKorea

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