Abstract: Keystroke dynamics is a biometric that has been explored as a means of making user authentication more secure. However, studies have indicated that the performance of such a system might be influenced by the demography of the user population. The purpose of this study is to investigate the relationship between the age and gender of the users of a keystroke dynamics-based mobile phone user authentication and the performance of the scheme. Using a mobile keystroke dynamics dataset containing the age and gender information of the participants, an anomaly detector algorithm was used to test whether an impostor user would have been recognised or not. A False Acceptance Rate (FAR) is calculated for the genuine user and impostors' combination. A Two-Way Analysis of Variance (ANOVA) was used to test the hypotheses whether there are significance differences and interaction between the FARs obtained with respect to the age group and gender categories. The result suggests that the age and gender of the users of a keystroke dynamics user authentication system on a mobile phone is not expected to have significant impact on the performance of such a system. Unlike previous studies that were based on keystroke dynamics data from desktop computer users, this investigation focused on keystroke dynamics for mobile phones. The results obtained in this paper has further improved our understanding that demographic bias relating to age and gender may be eliminated from the concerns that may arise from the use of a keystroke dynamics user authentication on a mobile phone.
Keywords: Biometrics, User Authentication, Keystroke Dynamics, Classification, Machine Learning.
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DOI:
10.17148/IJARCCE.2021.10903
