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User verification based on the analysis of keystrokes while using various software

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents the new approach to a computer users verification. The research concerns an analysis of user’s continuous activity related to a keyboard used while working with various software. This type of analysis constitutes a type of free-text analysis. The presented method is based on the analysis of users activity while working with particular computer software (e.g. text editors, utilities). A method of computer user profiling is proposed and an attempt to intrusion detection based on k-NN classifier is performed. The obtained results show that the introduced method can be used in the intrusion detection and monitoring systems. Such systems are especially needed in medical facilities where sensitive data are processed.
Rocznik
Tom
Strony
13--22
Opis fizyczny
Bibliogr. 17 poz., rys., tab., wykr.
Twórcy
  • University of Silesia, Institute of Computer Science, ul. Bedzinska 39, 41-200 Sosnowiec, Poland
autor
  • University of Silesia, Institute of Computer Science, ul. Bedzinska 39, 41-200 Sosnowiec, Poland
Bibliografia
  • [1] ALSULTAN A., WARWICK K. Keystroke dynamics authentication: A survey of free-text methods. IJCSI International Journal of Computer Science Issues, July 2013, Vol. 10. pp. 1–10.
  • [2] BANERJEE S., WOODARD D. Biometric authentication and identification using keystroke dynamics: A survey. Journal of Pattern Recognition Research, 2012, Vol. 7. pp. 116–139.
  • [3] HU J., GINGRICH D., SENTOSA A. A k-Nearest Neighbor approach for user authentication through biometric keystroke dynamics. IEEE International Conference on Communications, 2008. pp. 1556–1560.
  • [4] KILLOURHY K., MAXION R. Comparing anomaly-detection algorithms for keystroke dynamics. International Conference on Dependable Systems & Networks (DSN-09), 2009. IEEE Computer Society Press, pp. 125–134.
  • [5] KUDŁACIK P., PORWIK P. A new approach to signature recognition using the fuzzy method. Pattern Analysis & Applications, 2014, Vol. 17. pp. 451–463.
  • [6] KUDŁACIK P., PORWIK P., WESOŁOWSKI T. Fuzzy approach for intrusion detection based on user’s commands. Soft Computing, 2015. Springer-Verlag Berlin Heidelberg.
  • [7] LOPATKA M., PEETZ M. Vibration sensitive keystroke analysis. Proceedings of The 18th Annual Belgian-Dutch Conference on Machine Learning, 2009. pp. 75–80.
  • [8] PALYS M., DOROZ R., PORWIK P. On-line signature recognition based on an analysis of dynamic feature. IEEE International Conference on Biometrics and Kansei Engineering, 2013. Tokyo Metropolitan University Akihabara, pp. 103– 107.
  • [9] 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, 2014, Vol. 17.
  • [10] RAIYN J. A survey of cyber attack detection strategies. International Journal of Security and Its Applications, 2014, Vol. 8. pp. 247–256.
  • [11] SALEM M., HERSHKOP S., STOLFO S. A survey of insider attack detection research. Advances in Information Security, 2008, Vol. 39. Springer US, pp. 69–90.
  • [12] TAPPERT C., VILLIANI M., CHA S. Keystroke biometric identification and authentication on long-text input. Behavioral Biometrics for Human Identification: Intelligent Applications, 2010. pp. 342–367.
  • [13] TEH P. S., TEOH A. B. J., YUE S. A survey of keystroke dynamics biometrics. The Scientific World Journal, 2013, Vol. 2013. p. 24 pages.
  • [14] WESOŁOWSKI T., PALYS M., KUDŁACIK P. Computer user verification based on mouse activity analysis. New Trends in Intelligent Information and Database Systems, 2015, Vol. 598 of Studies in Computational Intelligence. Springer International Publishing, pp. 61–70.
  • [15] WESOŁOWSKI T. E., PORWIK P. Computer user profiling based on keystroke analysis. Advanced Computing and Systems for Security, 2015, Vol. 395 of Advances in Intelligent Systems and Computing. Springer.
  • [16] WESOŁOWSKI T. E., PORWIK P. Keystroke data classification for computer user profiling and verification. Computational Collective Intelligence, 2015, Vol. 9330 of Lecture Notes in Computer Science. Springer International Publishing, pp. 588–597.
  • [17] ZHONG Y., DENG Y., JAIN A. Keystroke dynamics for user authentication. Computer Vision and Pattern Recognition Workshops, IEEE Computer Society Conference, 2012. pp. 117–123.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-66cc99ed-4065-4138-a648-9556df0b9dca
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