National Interests: Priorities and Security
 

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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

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