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User Attribution Through Keystroke Dynamics-Based Author Age Estimation.

Tsimperidis, I., Rostami, S., Wilson, K. and Katos, V., 2021. User Attribution Through Keystroke Dynamics-Based Author Age Estimation. In: INC 2020: 12th International Networking Conference, 19-21 September 2020, Online, 47 - 61.

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DOI: 10.1007/978-3-030-64758-2_4

Abstract

Keystroke dynamics analysis has often been used in user authentication. In this work, it is used to classify users according to their age. The authors have extended their previous research in which they managed to identify the age group that a user belongs to with an accuracy of 66.1%. The main changes made were the use of a larger dataset, which resulted from a new volunteer recording phase, the exploitation of more keystroke dynamics features, and the use of a procedure for selecting those features that can best distinguish users according to their age. Five machine learning models were used for the classification, and their performance in relation to the number of features involved was tested. As a result of these changes in the research method, an improvement in the performance of the proposed system has been achieved. The accuracy of the improved system is 89.7%.

Item Type:Conference or Workshop Item (Paper)
ISSN:2367-3370
Uncontrolled Keywords:Keystroke dynamics dataset; User age classification; Feature selection; Information gain; RBFN; AUC; Digital evidence
Group:Faculty of Science & Technology
ID Code:36304
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:29 Nov 2021 12:14
Last Modified:29 Nov 2021 12:14

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