Title: A comprehensive study of machine learning approaches for keystroke dynamics authentication

Authors: Tanya Teotia; Mridula Sharma; Haytham Elmiligi

Addresses: Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8, Canada ' Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8, Canada ' Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8, Canada

Abstract: The most popular behavioural biometrics that is currently being considered as a second factor of authentication is keystroke dynamics. However, the adoption of this authentication technology faces several challenges, such as lack of a standard benchmark and evaluation methodology that could be used to compare the accuracy and performance of different frameworks. In this paper, we provide a comprehensive design space exploration of various machine learning frameworks to authenticate users based on keystroke dynamics. The paper also studies the machine learning design flow, discusses details of every single step in the process, and provides comparative analysis of possible options available for developers. The paper presents a comparative analysis of various machine learning frameworks supported by experimental analysis. Our experimental work analyses the efficiency of various machine algorithms, compares the impact of filter-based and wrapper-based feature selection techniques, and compares the accuracy of machine learning classifiers by using different feature sets.

Keywords: machine learning; keystroke dynamics; classification; feature extraction; feature selection.

DOI: 10.1504/IJBM.2023.133148

International Journal of Biometrics, 2023 Vol.15 No.5, pp.560 - 586

Received: 14 May 2021
Accepted: 06 Mar 2022
Published online: 01 Sep 2023 *

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