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Article

Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

1
Department of Electrical and Computer Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
2
Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Jemal H. Abawajy
Electronics 2021, 10(7), 835; https://doi.org/10.3390/electronics10070835
Received: 20 February 2021 / Revised: 23 March 2021 / Accepted: 26 March 2021 / Published: 31 March 2021
(This article belongs to the Special Issue Advances on Networks and Cyber Security)
Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers. View Full-Text
Keywords: keystroke dynamics; data mining; user classification; feature selection; feature comparison keystroke dynamics; data mining; user classification; feature selection; feature comparison
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MDPI and ACS Style

Tsimperidis, I.; Yucel, C.; Katos, V. Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes. Electronics 2021, 10, 835. https://doi.org/10.3390/electronics10070835

AMA Style

Tsimperidis I, Yucel C, Katos V. Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes. Electronics. 2021; 10(7):835. https://doi.org/10.3390/electronics10070835

Chicago/Turabian Style

Tsimperidis, Ioannis, Cagatay Yucel, and Vasilios Katos. 2021. "Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes" Electronics 10, no. 7: 835. https://doi.org/10.3390/electronics10070835

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