Abstract
In this paper we discuss an applied problem of continuous user authentication based on keystroke dynamics. It is important for a user model to discover new intruders. That means we don’t have the keystroke samples of such intruders on the training phase. It leads us to the necessity of using one-class models. In the paper we review some popular feature extraction, preprocessing and one-class classification methods for this problem. We propose a new approach to reduce dimensionality of a feature space based on two-sample Kolmogorov-Smirnov test and investigate how the quantile-based discretization technique can improve the one-class models’ performance. We present two algorithms, which have not been used for keystroke dynamics before: Fuzzy kernel-based classifier and Random Forest Regression classifier. We conduct experimental evaluation of the proposed approach.
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Acknowledgments
The research is financially supported by the Ministry of Education and Science of the Russian Federation (the subsidy agreement #14.604.21.0056, unique project identifier RFMEFI60414X0056).
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Kazachuk, M. et al. (2016). One-Class Models for Continuous Authentication Based on Keystroke Dynamics. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_45
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DOI: https://doi.org/10.1007/978-3-319-46257-8_45
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