Abstract
Keystroke dynamics is a method of discriminating users based on their typing patterns, and can be used as a second factor in uniquely identifying users when they type their passwords. Most studies that have been performed in this domain focus on devising new techniques to design more accurate systems, but little attention has been given to make them practically feasible in real-life applications. A key limitation is the scarce availability of training samples from a user.
We have designed a framework that accepts only 10 training samples from a user, and synthesises 190 more training samples from these using Synthetic Minority Oversampling Technique (SMOTE). The accuracy of this framework is comparable to those frameworks that use a large number of training samples collected from a user and are not practical in real-life applications. We used the standard and freely available CMU dataset and their evaluation methodologies as a benchmark.
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Payal, S., Garware, B., Kelkar, S. (2018). Towards Designing a Framework for Practical Keystroke Dynamics Based Authentication. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_24
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DOI: https://doi.org/10.1007/978-3-319-60618-7_24
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