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National Interests: Priorities and Security
 

User identification through keystroke dynamics as part of automated proctoring systems

Vol. 16, Iss. 3, MARCH 2020

Received: 23 October 2019

Received in revised form: 14 November 2019

Accepted: 6 December 2019

Available online: 16 March 2020

Subject Heading: THREATS AND SECURITY

JEL Classification: I21, Y90

Pages: 582–596

https://doi.org/10.24891/ni.16.3.582

Abzalov A.R. Kazan National Research Technical University named after A.N. Tupolev – KAI (KNRTU-KAI), Kazan, Republic of Tatarstan, Russian Federation
abzalov@land.ru

ORCID id: not available

Zhiganov A.V. Kazan National Research Technical University named after A.N. Tupolev – KAI (KNRTU-KAI), Kazan, Republic of Tatarstan, Russian Federation
Lesha.159@yandex.ru

ORCID id: not available

Samigullina R.R. Kazan National Research Technical University named after A.N. Tupolev – KAI (KNRTU-KAI), Kazan, Republic of Tatarstan, Russian Federation
Samigullina0304@mail.ru

ORCID id: not available

Subject Popular online courses and testing programs integrate into correspondence education systems, which are more often than not based on automated proctoring. What makes the latter vulnerable is user identification.
Objectives We examine user identification methods through keystroke dynamics and devise a more accurate and effective technique for user identification through keystroke dynamics.
Methods The article sets out a three-tiered model for identifying users more accurately not only in automated proctoring environments, but also in critically sensitive locations.
Results We had an experiment, which showed a 97.5 percent accuracy of user identification. We significantly reduced illegitimate users at the statistical level of the three-tiered model.
Conclusions and Relevance Following the study, it is possible to develop a logic comparison method for higher accuracy. It will serve for creating a more refined model, which would accommodate for distinctions of each user and some deviations of users’ emotions. This would contribute to continuous user identification systems to monitor their emotional condition at critical locations.

Keywords: keystroke dynamics, biometrics, authentication, automated proctoring, clustering

References:

  1. Glova V.I., Katasev A.S., Abzalov A.R. [Model for localization of distortions and authorship acknowledgement of source program texts]. Vestnik Kazanskogo gosudarstvennogo tekhnicheskogo universiteta im. A.N. Tupoleva = Vestnik of Kazan State Technical University named after A.N. Tupolev, 2008, no. 3, pp. 84–87. (In Russ.)
  2. Dobrovinskii D.S., Lovetskii I.V., Popov M.A. [Proctoring as a tool of distance education development]. Nauchno-tekhnicheskoe i ekonomicheskoe sotrudnichestvo stran ATR v XXI veke = Scientific, Technical and Economic Cooperation of the Asia-Pacific Countries in the 21st Century, 2018, vol. 2, pp. 27–32. (In Russ.)
  3. Klimenskikh M.V., Istomin D.V., Khalfin A.B., Panchenko V.N. [Provision of procedure in distance examination by the methods of identification student personality]. Vestnik Tekhnologicheskogo universiteta = Bulletin of the Kazan National Research Technological University, 2016, no. 3, pp. 134–151. (In Russ.)
  4. Kostyuchenko E.Yu., Meshcheryakov R.V. [User recognition by handwriting from a keyboard on a fixed key phrase in computer systems]. Izvestiya YuFU. Tekhnicheskie nauki = Izvestiya SfedU. Engineering Sciences, 2003, no. 4, pp. 177–178. URL: Link (In Russ.)
  5. Tumbinskaya M.V., Bayanov B.I., Rakhimov R.Zh. et al. [Analysis and forecast of undesirable cloud services traffic]. Biznes-informatika = Business Informatics, 2019, no. 1, pp. 71–81. (In Russ.) URL: Link
  6. Sharipov R.R., Katasev A.S., Kirpichnikov A.P. [Methods for analyzing keystroke dynamics of users through the model Gaussian signals]. Vestnik Tekhnologicheskogo universiteta = Bulletin of the Kazan National Research Technological University, 2016, no. 13, pp. 157–160. URL: Link (In Russ.)
  7. Tumbinskaya M.V. [Protection of information in social networks from socio-engineer attacks of the attacker]. Prikladnaya informatika = Applied Informatics, 2017, vol. 12, no. 3, pp. 88–102. (In Russ.)
  8. Sharipov R.R., Katasev A.S. [Analyzing keystroke dynamics of users in ICT systems through the poly-Gaussian algorithm]. Informatsiya i bezopasnost' = Information and Security, 2016, no. 4, pp. 587–590. (In Russ.)
  9. Tumbinskaya M.V [System approach to protection against unwanted information in the social networks]. Voprosy kiberbezopasnosti, 2017, no. 2, p. 30–44. URL: Link (In Russ.)
  10. Tumbinskaya M.V. [Secure information system model of Internet banking]. Prikladnaya informatika = Applied Informatics, 2015, no. 5, pp. 62–72. URL: Link (In Russ.)
  11. Sharipov R.R., Safiullin N.Z. [Hardware analysis of keyboard script by using the reference Gaussian signals]. Vestnik Kazanskogo gosudarstvennogo tekhnicheskogo universiteta im. A.N. Tupoleva = Vestnik of Kazan State Technical University named after A.N. Tupolev, 2006, no. 2, pp. 21–23. (In Russ.)
  12. Sharipov R.R., Katasev A.S. [User keyboard handwriting recognition system based on poly Gaussian algorithm]. Vestnik Kazanskogo gosudarstvennogo energeticheskogo universiteta = Bulletin of Kazan State Power Engineering University, 2016, no. 4, pp. 45–59. URL: Link (In Russ.)
  13. Tumbinskaya M.V. [Providing protection from targeted information in social networks]. Vestnik Mordovskogo universiteta = Mordovia University Bulletin Journal, 2017, vol. 27, no. 2, pp. 264–288. (In Russ.)
  14. Mukhamatkhanov R.M., Mikhailov A.A., Bayanov B.I., Tumbinskaya M.V. [Classification of DDoS attacks based on the neural network model]. Prikladnaya informatika = Applied Informatics, 2019, no. 1, pp. 96–103. (In Russ.)
  15. Kormil'tsev N.V., Uvarov A.D., Khamatnurov I.I., Tumbinskaya M.V. [Analysis of the protection of the LT-A security system from the targeted exposure of DDoS attack]. Natsional'nye interesy: prioritety i bezopasnost' = National Interests: Priorities and Security, 2019, vol. 15, iss. 2, pp. 376–392. (In Russ.) URL: Link
  16. Aleksandrova L.A., Tumbinskaya M.V. [Interactive Learning System Model]. Programmnye produkty i sistemy = Software and Systems, 2009, no. 2, p. 39. URL: Link (In Russ.)

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