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Tytuł artykułu

Hybrid verification method based on finger-knuckle analysis and keystroke dynamics

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The increasing number of personal data leaks becomes one of the most important security issues hence the need to develop modern computer user verification methods. In the article, a potential of biometric methods fusion for continuous user verification was assessed. A hybrid approach for user verification based on fusion of keystroke dynamics and knuckle images analysis was presented. Verification is performed by a classification module where an ensemble classifier was used to verify the identity of a user. A proposed classifier works on a database which comprises of knuckle images and keyboard events for keystroke dynamics. The proposed approach was tested experimentally. The obtained results confirm that the proposed hybrid approach performs better than methods based on single biometric feature hence the introduced method can be used for increasing a protection level of computer resources against forgers and impostors. The paper presents results of preliminary research conducted to assess the potential of biometric methods fusion.
Rocznik
Tom
Strony
26--36
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
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
autor
  • 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] BANERJEE S., WOODARD D. Biometric authentication and identification using keystroke dynamic: a survey. Journal of Pattern Recognition Research, 2012, Vol. 7. pp. 116–139.
  • [2] CHOON-CHING N., HOON Y. M., COSTEN N., LI B. Automatic wrinkle detection using hybrid hessian filter. Lecture Notes in Computer Science, 2015, Vol. 9005. pp. 609–622.
  • [3] DOROZ R., ET AL. A new personal verification technique using finger–knuckle imaging. Lecture Notes in Computer Science, 2016, Vol. 9876. pp. 515–524.
  • [4] DOROZ R., PORWIK P., SAFAVERDI H. The new multilayer ensemble classifier for verifying users based on keystroke dynamics. Lecture Notes in Computer Science, 2015, Vol. 9330. pp. 598–605.
  • [5] FERRER M., TRAVIESO C., ALONSO J. Using hand knuckle texture for biometric identifications. IEEE Aerospace and Electronic Systems Magazine, 2006, Vol. 21(6). pp. 23–27.
  • [6] KUDLACIK P., PORWIK P., WESOLOWSKI T. Fuzzy approach for intrusion detection based on user’s commands. Soft Computing, 2016, Vol. 20. pp. 2705–2719.
  • [7] KUMAR A., WANG B. Recovering and matching minutiae patterns from finger knuckle images. Pattern Recognition Letters, 2015, Vol. 68. pp. 361–367.
  • [8] KUMAR A., ZHOU Y. Human identification using knuckle codes. IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009. pp. 98–109.
  • [9] MARKOVSKY I., MAHMOODI S. Least-squares contour alignment. IEEE Signal Processing Letters, 2009, Vol. 16(1). pp. 41–44.
  • [10] MORALES A., TRAVIESO C., FERRER M., ALONSO J. Improved finger-knuckle-print authentication based on orientation enhancement. Electronics Letters, 2011, Vol. 47(6). pp. 380–382.
  • [11] OTSU N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 1979, Vol. 9(1). pp. 62–66.
  • [12] PAVLIDIS T. A thinning algorithm for discrete binary images. Computer Graphics and Image Processing, 1980, Vol. 13(2). pp. 142–157.
  • [13] PORWIK P., DOROZ R., WROBEL K. A new signature similarity measure. Proceedings of World Congress on Nature and Biologically Inspired Computing, NABIC 2009, 2009. pp. 1022–1027.
  • [14] RAIYN J. A survey of cyber attack detection strategies. International Journal of Security and its Applications, 2014, Vol. 8(1). pp. 247–256.
  • [15] SALEM M., HERSHKOP S., STOLFO S. A survey of insider attack detection research. Advances in Information Security, 2008, Vol. 39. pp. 69–90.
  • [16] WESOLOWSKI T., PORWIK P. Keystroke data classification for computer user profiling and verification. Lecture Notes in Artificial Intelligence, 2015, Vol. 9330. pp. 588–597.
  • [17] WESOLOWSKI T., PORWIK P., DOROZ R. Electronic health record security based on ensemble classification of keystroke dynamics. Applied Artificial Intelligence, 2016, Vol. 30. pp. 521–540.
  • [18] WESOLOWSKI T. E., PORWIK P. Computer user profiling based on keystroke analysis. Advances in intelligent systems and computing, 2016, Vol. 395. pp. 3–13.
  • [19] WESOLOWSKI T. E., PORWIK P., DOROZ R. Electronic health record security based on ensemble classification of keystroke dynamics. Applied Artificial Intelligence, 2016, Vol. 20. pp. 521–540.
  • [20] WROBEL K., PORWIK P., DOROZ R., SAFAVERDI H. Person verification based on finger knuckle images and least-squares contour alignment. International Conference on Biometrics and Kansei Engineering (ICBAKE), 2017. pp. 119–122.
  • [21] XIONG M., YANG W., SUN C. Finger-knuckle-print recognition using lgbp. Lecture Notes in Computer Science, 2011, Vol. 6676. pp. 270–277.
  • [22] 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
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4af4f7c8-60bf-404e-9563-b926bdb18c8b
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