The New Multilayer Ensemble Classifier for Verifying Users Based on Keystroke Dynamics

  • Rafal DorozEmail author
  • Piotr Porwik
  • Hossein Safaverdi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9330)


In this work we proposed the new multilayer ensemble classifier which can be applied in many domains, especially in the biometric systems. Proposed classifier works on database which comprises data from keystroke dynamics. Such kind of data allows us to recognize computer users who use password. It is a typical case among the users every day work. Obtained results confirm that proposed multilayer ensemble classifier gives the high security level. For this reason our method can be used to protect computer resources against forgers and imposters.


Keystroke dynamics Ensemble classifiers Biometrics 


  1. 1.
    Doroz, R., Porwik, P.: Handwritten signature recognition with adaptive selection of behavioral features. In: Chaki, N., Cortesi, A. (eds.) Computer Information Systems – Analysis and Technologies. CCIS, vol. 245, pp. 128–136. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Guven, A., Sogukpinar, I.: Understanding users’ keystroke patterns for computer access security. Computers Security 22(8), 695–706 (2003)CrossRefGoogle Scholar
  3. 3.
    Kirkby R.: Improving Hoeffding Trees, PhD thesis, University of Waikato (2007)Google Scholar
  4. 4.
    Kang, P., Cho, S: Keystroke dynamics-based user authentication using long and free text strings from various input devices, Information Sciences 308, 72–93 (2015)Google Scholar
  5. 5.
    Loy, C.C., Lim, C.P., Lai, W.K.: Pressure-based typing biometrics user authentication using the fuzzy ARTMAP neural network. In: International Conference on Neural Information Processing, Taiwan (2005)Google Scholar
  6. 6.
    Loy, C.C., Lai, W.K., Lim, C.P: Keystroke patterns classification using the ARTMAP-FD neural network. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Taiwan (2007)Google Scholar
  7. 7.
    Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems 16(4), 351–359 (2000)CrossRefGoogle Scholar
  8. 8.
    Panasiuk, P., Saeed, K.: Influence of database quality on the results of keystroke dynamics algorithms. In: Chaki, N., Cortesi, A. (eds.) CISIM 2011. CCIS, vol. 245, pp. 105–112. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    Porwik, P., Doroz, R., Wrobel, K.: A new signature similarity measure. In: World Congress on Nature Biologically Inspired Computing, NaBIC 2009, pp. 1022–1027. IEEE (2009)Google Scholar
  10. 10.
    Porwik, P., Doroz, R., Orczyk, T.: The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition. Pattern Analysis and Applications, 1–19 (2015)Google Scholar
  11. 11.
    Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 377–388. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  12. 12.
    Rybnik, M., Panasiuk, P., Saeed, K.: User authentication with keystroke dynamics using fixed text. In: International Conference on Biometrics and Kansei Engineering, ICBAKE 2009, pp. 70–75. IEEE (2009)Google Scholar
  13. 13.
    Rybnik, M., Panasiuk, P., Saeed, K., Rogowski, M.: Advances in the keystroke dynamics: the practical impact of database quality. In: Cortesi, A., Chaki, N., Saeed, K., Wierzchoń, S. (eds.) CISIM 2012. LNCS, vol. 7564, pp. 203–214. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  14. 14.
    Rybnik, M., Tabedzki, M., Saeed, K.: A keystroke dynamics based system for user identification. In: 7th Computer Information Systems and Industrial Management Applications, CISIM 2008, pp. 225–230. IEEE (2008)Google Scholar
  15. 15.
    Sung, K., Cho, S.: GA SVM Wrapper Ensemble for Keystroke Dynamics Authentication Department of Industrial Engineering, Seoul National University, San 56–1, Shillim-dong, Kwanak-gu, Seoul, 151–744, Korea (2005)Google Scholar
  16. 16.
    SZS, I., Cherrier, E., Rosenberger, C., Bours, P.: Soft biometrics for keystroke dynamics: Profiling individuals while typing passwords. Computers Security 45, 147–155 (2014)CrossRefGoogle Scholar
  17. 17.
    Teh, P.S., Teoh, A.B.J., Tee, C., Ong, T.S.: Keystroke dynamics in password authentication enhancement. Expert Systems with Applications 37(12), 8618–8627 (2010)CrossRefGoogle Scholar
  18. 18.
    Wrobel, K., Doroz, R., Palys, M.: A method of lip print recognition based on sections comparison. In: IEEE Int. Conference on Biometrics and Kansei Engineering, pp. 47–52. Tokyo Metropolitan University Akihabara, Tokyo, Japan (2013)Google Scholar
  19. 19.
    Wrobel, K., Doroz, R., Palys, M.: Lip print recognition method using bifurcations analysis. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS, vol. 9012, pp. 72–81. Springer, Heidelberg (2015) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaKatowice, SosnowiecPoland

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