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

Authors: Tsimperidis, I., Yucel, C. and Katos, V.

Journal: Electronics (Switzerland)

Volume: 10

Issue: 7

eISSN: 2079-9292

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.

https://eprints.bournemouth.ac.uk/35379/

Source: Scopus

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

Authors: Tsimperidis, I., Yucel, C. and Katos, V.

Journal: ELECTRONICS

Volume: 10

Issue: 7

eISSN: 2079-9292

DOI: 10.3390/electronics10070835

https://eprints.bournemouth.ac.uk/35379/

Source: Web of Science (Lite)

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

Authors: Tsimperidis, I., Yucel, C. and Katos, V.

Journal: Electronics

Volume: 10

Issue: 7

ISSN: 2079-9292

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.

https://eprints.bournemouth.ac.uk/35379/

Source: BURO EPrints