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Person verification based on keystroke dynamics

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a new multilayer ensemble of classifiers for users verification who use computer keyboard. The special keyboard extracts the key pressure and latency between keyboard keys pressed during password entered. When user is typing password the system creates a pattern based on time and key pressure. For users verification group of classifiers have been proposed. It allows to obtain the higher accuracy level compared to alternative techniques. The efficiency of the proposed method has been confirmed in the experiments carried out.
Rocznik
Tom
Strony
39--44
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
autor
  • University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland
Bibliografia
  • [1] CHANG C.-C., LIN C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., May 2011, Vol. 2. ACM, New York, NY, USA, pp. 27:1–27:27.
  • [2] DOROZ R., PORWIK P. Handwritten signature recognition with adaptive selection of behavioral features. Computer Information Systems Analysis and Technologies, 2011, Vol. 245 of Communications in Computer and Information Science. Springer Berlin Heidelberg, pp. 128–136.
  • [3] DOROZ R., PORWIK P., SAFAVERDI H. The new multilayer ensemble classifier for verifying users based on keystroke dynamics. Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II, 2015. pp. 598–605.
  • [4] GUVEN A., SOGUKPINAR I. Understanding users’ keystroke patterns for computer access security. Computers & Security, 2003, Vol. 22. pp. 695–706.
  • [5] IDRUS S. Z. S., CHERRIER E., ROSENBERGER C., BOURS P. Soft biometrics for keystroke dynamics: Profiling individuals while typing passwords. Computers & Security, 2014, Vol. 45. pp. 147 – 155.
  • [6] KANG P., CHO S. Keystroke dynamics-based user authentication using long and free text strings from various input devices. Information Sciences, 2015, Vol. 308. pp. 72–93.
  • [7] KIRKBY R. Improving hoeffding trees. 2007.
  • [8] LOY C. C., LAI W. K., LIM C. P. Keystroke patterns classification using the artmap-fd neural network. Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference on, 2007, Vol. 1. pp. 61–64.
  • [9] LOY C. C., PROF A., CHEE D., LIM P., LAI K., BERHAD M. 2005): ”pressure-based typing biometrics user authentication using the fuzzy ARTMAP neural network. Proc. of the 12th Int. Conf. on Neural Information Processing. pp. 647–652.
  • [10] MONROSE F., RUBIN A. D. Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems, 2000, Vol. 16. pp. 351–359.
  • [11] PANASIUK P., SAEED K. Influence of database quality on the results of keystroke dynamics algorithms. Computer Information Systems Analysis and Technologies, 2011, Vol. 245. Springer Berlin Heidelberg, pp. 105–112.
  • [12] PORWIK P., DOROZ R., ORCZYK T. The k-NN classifier and self-adaptive hotelling data reduction technique in handwritten signatures recognition. Pattern Anal. Appl., 2015, Vol. 18. pp. 983–1001.
  • [13] QUTEISHAT A., LIM C. P., LOY C. C., LAI W. K. Authenticating the identity of computer users with typing biometrics and the fuzzy Min-Max neural network BIOMETRICS AND ITS APPLICATIONS). Biomedical fuzzy and human sciences : the official journal of the Biomedical Fuzzy Systems Association, jan 2009, Vol. 14. Biomedical Fuzzy Systems Association, pp. 47–53.
  • [14] RYBNIK M., PANASIUK P., SAEED K. User authentication with keystroke dynamics using fixed text. Biometrics and Kansei Engineering, 2009. ICBAKE 2009. International Conference on, Jun 2009. pp. 70–75.
  • [15] RYBNIK M., PANASIUK P., SAEED K., ROGOWSKI M. Advances in the keystroke dynamics: The practical impact of database quality. Computer Information Systems and Industrial Management, 2012, Vol. 7564 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 203–214.
  • [16] RYBNIK M., TABEDZKI M., SAEED K. A keystroke dynamics based system for user identification. Computer Information Systems and Industrial Management Applications, 2008. IEEE-CISIM, Jun 2008. pp. 225–230.
  • [17] SUNG K.-S., CHO S. GA SVM wrapper ensemble for keystroke dynamics authentication. Advances in Biometrics, 2005, Vol. 3832 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 654–660.
  • [18] TEH P. S., TEOH A. B. J., TEE C., ONG T. S. Keystroke dynamics in password authentication enhancement. Expert Systems with Applications, 2010, Vol. 37. pp. 8618 – 8627.
  • [19] VIJAYARANI S., MUTHULAKSHMI M. Comparative analysis of bayes and lazy classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 2013, Vol. 2. pp. 3118–3124.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d359286f-0ede-40c5-ac41-a77c81db73d2
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