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    R2BN: an adaptive model for keystroke-dynamics-based educational level classification

    Tsimperidis, I. and Yoo, Paul D. and Taha, K. and Mylonas, A. and Katos, V. (2018) R2BN: an adaptive model for keystroke-dynamics-based educational level classification. IEEE Transactions on Cybernetics , pp. 1-11. ISSN 2168-2267.

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    Abstract

    Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a num- ber of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual’s characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the edu- cational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers’ keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.

    Metadata

    Item Type: Article
    Additional Information: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Paul Yoo
    Date Deposited: 15 Oct 2018 14:36
    Last Modified: 01 Aug 2019 19:10
    URI: http://eprints.bbk.ac.uk/id/eprint/24457

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