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Age and gender as cyber attribution features in keystroke dynamic-based user classification processes.

Tsimperidis, I., Yucel, C. and Katos, V., 2021. Age and gender as cyber attribution features in keystroke dynamic-based user classification processes. Electronics, 10 (7), 835.

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DOI: 10.3390/electronics10070835

Abstract

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.

Item Type:Article
ISSN:2079-9292
Additional Information:This article belongs to the Special Issue Advances on Networks and Cyber Security
Uncontrolled Keywords:keystroke dynamics; data mining; user classification; feature selection; feature comparison
Group:Faculty of Science & Technology
ID Code:35379
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:13 Apr 2021 09:19
Last Modified:27 May 2021 07:54

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