Advertisement

User Keystroke Authentication and Recognition of Emotions Based on Convolutional Neural Network

  • Ihor TereikovskyiEmail author
  • Liudmyla Tereikovska
  • Oleksandr Korystin
  • Shynar Mussiraliyeva
  • Aizhan Sambetbayeva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)

Abstract

The article is devoted to the problem of improving Biometric identification systems based on Keystroke Dynamics for recognizing emotions and authenticating users of information systems through the implementation of modern neural network solutions based on Convolutional Neural Network (CNN). It is established that the difficulties of such implementation are associated with coding the keystroke parameters to a form suitable for CNN processing. A coding procedure based on the presentation of fixed-size keystroke parameters in the form of a color square image is proposed. Each encoded text symbol corresponds to a separate point of the image and is characterized using the corresponding ASCII code and keystroke parameters such as the key hold time and the time between keystrokes. Experimental studies showed that the proposed coding procedure made it possible to use CNN for analyzing Keystroke Dynamics and achieve recognition error of emotions and personality at the level of the best modern recognition systems.

Keywords

Recognition of emotions Biometric authentication Keystroke Dynamics Convolutional neural network 

References

  1. 1.
    Gnatyuk, S.: Critical aviation information systems cybersecurity. In: Meeting Security Challenges Through Data Analytics and Decision Support, NATO Science for Peace and Security Series, D: Information and Communication Security, vol. 47, no. 3, pp. 308–316. IOS Press Ebooks (2016)Google Scholar
  2. 2.
    Gnatyuk, S., Sydorenko, V., Aleksander, M.: Unified data model for defining state critical information infrastructure in civil aviation. In: Proceedings of the 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kyiv, Ukraine, 24–27 May 2018, pp. 37–42 (2018)Google Scholar
  3. 3.
    Kobojek, P., Saeed, K.: Application of recurrent neural networks for user verification based on keystroke dynamics. J. Telecommun. Inf. Technol. N3, 80–90 (2016)Google Scholar
  4. 4.
    Ivanov, A.I.: Nejrosetevye algoritmy biometricheskoj identifikacii lichnosti. Kn. 15: Monografiya/A.I. Ivanov. – M.: Radiotehnika (2004). 144 sGoogle Scholar
  5. 5.
    Koshevaya, N.A., Maznichenko, N.I.: Podhod k povysheniyu nadezhnosti identifikacii polzovatelej kompyuternyh sistem po dinamike napisaniya parolej Sistemi obrobki informaciyi, vipusk 6(122 c), 140–146 (2014)Google Scholar
  6. 6.
    Savinov, A.N. Matematicheskaya model mehanizma raspoznavaniya klaviaturnogo pocherka na osnove Gaussovskogo raspredeleniya/A.N. Savinov, I.G. Sidorkina // Izvestiya Kabardino-Balkarskogo nauchnogo centra RAN. Vyp. I. - Nalchik: Kabardino-Balkarskij nauchnyj centr RAN, - S, pp. 26–32 (2013)Google Scholar
  7. 7.
    Aitchanov, B., Korchenko, A., Tereykovskiy, I., Bapiyev, I.: Perspectives for using classical neural network models and methods of counteracting attacks on network resources of information systems. News of the national academy of sciences of the republic of Kazakhstan series of geology and technical sciences 2017, vol. 5, no. 425, pp. 202–212 (2017)Google Scholar
  8. 8.
    Maheshwary, S., Ganguly, S., Pudi, V.: Deep secure: a fast and simple neural network based approach for user authentication and identification via keystroke dynamics. In: Conference: IWAISe, International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, pp. 34–40 (2017)Google Scholar
  9. 9.
    Dychka, I., Tereikovskyi, I., Tereikovska, L., Pogorelov, V., Mussiraliyeva, S.: Deobfuscation of computer virus malware code with value state dependence graph. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 370–379. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-91008-6_37CrossRefGoogle Scholar
  10. 10.
    Tereikovskyi, I., Chernyshev, D., Tereikovska, L.A., Mussiraliyeva, S., Akhmed, G.: The procedure for the determination of structural parameters of a convolutional neural network to fingerprint recognition. J. Theor. Appl. Inf. Technol. 97(8), 2381–2392 (2019)Google Scholar
  11. 11.
    Akhmetov, B., Tereykovsky, I., Doszhanova, A., Tereykovskaya, L.: Determination of input parameters of the neural network model, intended for phoneme recognition of a voice signal in the systems of distance learning. Int. J. Electron. Telecommun. 64(4), 425–432 (2018)Google Scholar
  12. 12.
    Alghamdi, S.J., Elrefaei, L.A.: Dynamic user verification using touch keystroke based on medians vector proximity. In: 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp. 121–126. IEEE (2015)Google Scholar
  13. 13.
    Bo, C., Zhang, L., Jung, T., Han, J., Li, X.-Y., Wang, Y.: Continuous user identification via touch and movement behavioral biometrics. In: 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2014)Google Scholar
  14. 14.
    Deng, Y., Zhong, Y.: Keystroke dynamics advances for mobile devices using deep neural network. GCSR 2, 59–70 (2015)Google Scholar
  15. 15.
    Xiaofeng, L., Shengfei, Z., Shengwei, Y.: Continuous authentication by free-text keystroke based on CNN plus RNN. Proc. Comput. Sci. 147, 314–318 (2019)CrossRefGoogle Scholar
  16. 16.
    Liu, M., Guan, J.: User keystroke authentication based on convolutional neural network. Commun. Comput. Inf. Sci. 971, 157–168 (2019)Google Scholar
  17. 17.
    Lin, C.-H., Liu, J.-C., Lee, K.-Y.: On neural networks for biometric authentication based on keystroke dynamics. Sens. Mater. 30(3), 385–396 (2018)Google Scholar
  18. 18.
    Çeker, H., Upadhyaya, S.: Sensitivity analysis in keystroke dynamics using convolutional neural networks. In: 2017 IEEE Workshop on Information Forensics and Security (WIFS), 4–7 December 2017, pp. 1–6 (2017)Google Scholar
  19. 19.
    Tereykovska, L., Tereykovskiy, I., Aytkhozhaeva, E., Tynymbayev, S., Imanbayev, A.: Encoding of neural network model exit signal, that is devoted for distinction of graphical images in biometric authenticate systems. News of the national academy of sciences of the republic of Kazakhstan series of geology and technical sciences 2017, vol. 6, no. 426, pp. 217–224 (2017)Google Scholar
  20. 20.
    Malik, J., Girdhar, D., Dahiya, R., Sainarayanan, G.: Reference threshold calculation for biometric authentication. Int. J. Image, Graph. Signal Process. (IJIGSP), 6(2), 46–53 (2014)CrossRefGoogle Scholar
  21. 21.
    Oyedotun, O.K., Dimililer, K.: Pattern recognition: invariance learning in convolutional auto encoder network. Int. J. Image, Graph. Signal Process. (IJIGSP) 8(3), 19–27 (2016)CrossRefGoogle Scholar
  22. 22.
    Sassi, A., Ouarda, W., Amar, C.B., Miguet, S.: Sky-CNN: a CNN-based learning approach for skyline scene understanding. Int. J. Intell. Syst. Appl. (IJISA), 11(4), 14–25 (2019)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KievUkraine
  2. 2.Kyiv National University of Construction and ArchitectureKievUkraine
  3. 3.Scientifically Research Institute of the Ministry of Internal AffairsKievUkraine
  4. 4.Al-Farabi Kazakh National UniversityAlmatyKazakhstan

Personalised recommendations