Advertisement

New Method for Digital Economy User’s Protection

  • Valery KonyavskyEmail author
  • Gennady RossEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 78)

Abstract

This paper analyses the existing approaches to the identification/authentication of users in the digital economy systems, and consideres options for functioning in trusted/untrusted environments. There is shown that currently known biometric features, which are effectively used in forensic science, do not allow the digital economy to guarantee protection against the influence of malicious software. As result of this study a new approach have been determined to the specific security requirements for computer systems in the digital economy conditions.

This approach will allow to establish the authenticity of the data source and increase the reliability of identification, and the combination of several biometric modalities, supplemented by an analysis of at least one of the possible physiological (reflex) reactions, will significantly increase the accuracy of biometric identification and provide a solution to the vital problem. As the result of this research, a Patent was obtained for a new “Interactive method of biometric user authentication”.

Keywords

Identification Authentication Digital citizen Digital economy Trusted environment Untrusted environment Static and dynamic behavioral signs Neural networks 

References

  1. 1.
    Liechtenstein, V.E., Konyavsky, V.A., Ross, G.V., Los’, V.P.: Multi-agent systems: self-organization and development, p. 264. Finance and Statistics, Moscow (2018)Google Scholar
  2. 2.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  3. 3.
    Bednarik, R., Kinnunen, T., Mihaila, A., Fränti, P.: Eye-movements as a biometric. In: Kalviainen, H., et al. (eds.) Scandinavian Conference on Image Analysis, pp. 780–789. Springer, Berlin, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Bargary, G., et al.: Individual differences in human eye movements: an oculomotor signature? Vision. Res. 141, 157–169 (2017)CrossRefGoogle Scholar
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In Proceedings: Advances in Neural Information Processing Systems, vol. 25, pp. 1090–1098 (2012)Google Scholar
  6. 6.
    Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA (2014)Google Scholar
  7. 7.
    Schroff, F., Kalenichenko, D., Philbin, J.W.: Facenet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA (2015)Google Scholar
  8. 8.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  9. 9.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning, Montréal, Montréal Canada (2014)Google Scholar
  10. 10.
    Konyavsky, V.A.: Interactive biometric user authentication method. The patent for the invention No. 2670648, 10.24.2018, bull. No. 30Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Plekhanov Russian University of EconomicsMoscowRussia
  2. 2.Financial UniversityMoscowRussia

Personalised recommendations