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Keystroke Pattern Authentication of Computer Systems Users as One of the Steps of Multifactor Authentication

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Advances in Computer Science for Engineering and Education II (ICCSEEA 2019)

Abstract

In this paper, an authentication program was created for Ukrainian-speaking users of computer systems based on their keyboard style. To develop the algorithm of this program, a series of experiments were conducted. Based on the results of the experiments, the optimal handwriting characteristics were selected, which were subsequently analyzed for the implementation of recognition, also the requirements for educational samples and the stages of their selection and preliminary processing are determined. Besides considered the most critical parameters, setting which significantly increases the likelihood of correct recognition. This system is proposed to use as one of the stages of multifactor authentication.

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References

  1. World market of the biometric systems, 2015–2022, 19 January 2017. http://json.tv/ict_telecom_analytics_view/mirovoy-rynok-biometricheskih-sistem-2015-2022-gg-20170119025618

  2. Arthur Galeev.: Almost all companies in the U.S. and Europe will use biometrics in two years, 30 March 2018. http://safe.cnews.ru/news/top/2018-03-26_pochti_vse_kompanii_v_ssha_i_evrope_budut_ispolzovat

  3. IBM Future of Identity Study: Millennials Poised to Disrupt Authentication Landscape, 29 January 2018. https://www-03.ibm.com/press/us/en/pressrelease/53646.wss

  4. Vysotska, O., Davydenko, A.: Classification of biometric authentication systems. Collection of Scientific Works of the Pukhov Institute for Modeling in Energy Engineering of NAS of Ukraine, vol. 27, pp. 108–114 (2004). (in Russian)

    Google Scholar 

  5. Vysotska, O., Davydenko, A.: Determination of critical parameters when choosing a biometric authentication system. Modeling and information technologies. Collection of Scientific Works of the Pukhov Institute for Modeling in Energy Engineering of NAS of Ukraine, vol. 27, pp. 80–86 (2004). (in Russian)

    Google Scholar 

  6. Benafia, A., Mazouzi, S., Sara, B.: Handwritten character recognition on focused on the segmentation of character prototypes in small strips. Int. J. Intell. Syst. Appl. (IJISA) 9(12), 29–45 (2017). https://doi.org/10.5815/ijisa.2017.12.04

    Article  Google Scholar 

  7. Hamd, M.H., Ahmed, S.K.: Biometric system design for Iris recognition using intelligent algorithms. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 10(3), 9–16 (2018). https://doi.org/10.5815/ijmecs.2018.03.02

    Article  Google Scholar 

  8. Zoubida, L., Adjoudj, R.: Integrating face and the both Irises for personal authentication. Int. J. Intell. Syst. Appl. (IJISA) 9(3), 8–17 (2017). https://doi.org/10.5815/ijisa.2017.03.02

    Article  Google Scholar 

  9. Angadi, S.A., Hatture, S.M.: Biometric person identification system: a multimodal approach employing spectral graph characteristics of hand geometry and palmprint. Int. J. Intell. Syst. Appl. (IJISA) 8(3), 48–58 (2016). https://doi.org/10.5815/ijisa.2016.03.06

    Article  Google Scholar 

  10. Kallan, R.: Basic concepts of neural networks. Translate from English. Publishing House “Williams” (2001). (in Russian)

    Google Scholar 

  11. Vysotska, O.: The influence of the elimination of educational data with gross errors on the dependence of the efficiency of probabilistic neural networks for authentication of computer systems users by keystroke pattern on various parameters. Modeling and information technologies. Collection of Scientific Works of the Pukhov Institute for Modeling in Energy Engineering of NAS of Ukraine, vol. 28, pp. 3–10 (2005). (in Russian)

    Google Scholar 

  12. Rao, G.A., Kishore, P.V.V., Kumar, D.A., Sastry, A.S.C.S.: Neural network classifier for continuous sign language recognition with selfie video. Far East J. Electron. Commun. 17(1), 49 (2017)

    Article  Google Scholar 

  13. Kishore, P.V.V., Rao, G.A., Kumar, E.K., Kumar, M.T.K., Kumar, D.A.: Selfie sign language recognition with convolutional neural networks. Int. J. Intell. Syst. Appl. (IJISA) 10(10), 63–71 (2018). https://doi.org/10.5815/ijisa.2018.10.07

    Article  Google Scholar 

  14. Hu, Z., Bodyanskiy, Y.V., Kulishova, N.Y., Tyshchenko, O.K.: A multidimensional extended neo-fuzzy neuron for facial expression recognition. Int. J. Intell. Syst. Appl. (IJISA) 9(9), 29–36 (2017). https://doi.org/10.5815/ijisa.2017.09.04

    Article  Google Scholar 

  15. Shimada, M., Iwasaki, S., Asakura, T.: Finger spelling recognition using neural network with pattern recognition model. In: SICE 2003 Annual Conference, vol. 3, pp. 2458–2463. IEEE (2003)

    Google Scholar 

  16. Hu, Z., Tereykovskiy, I.A., Tereykovska, L.O., Pogorelov, V.V.: Determination of structural parameters of multilayer perceptron designed to estimate parameters of technical systems. Int. J. Intell. Syst. Appl. (IJISA) 9(10), 57–62 (2017). https://doi.org/10.5815/ijisa.2017.10.07

    Article  Google Scholar 

  17. Rastorguev, S.P.: Program methods of information security. Teaching guide. Penza State University. Publishing House of Penza State University (2000). (in Russian)

    Google Scholar 

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Correspondence to Olena Vysotska .

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Vysotska, O., Davydenko, A. (2020). Keystroke Pattern Authentication of Computer Systems Users as One of the Steps of Multifactor Authentication. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-16621-2_33

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