Abstract
Keystroke dynamics is the analysis of timing information which describes exactly when each key was pressed or released by a user while typing. There have been various attempts to use this timing information to identify user’s typing pattern and authenticate the user in a system, but mostly from a research perspective. We present a new methodology which focuses on solving the practical problems associated with deploying a keystroke dynamics authentication system. In our proposed methodology, a user’s keystroke features are separated into two sets, namely high frequency and low frequency, based on the fraction of the total typing time the key takes. These two feature sets are then trained using OneClassSVM classifiers. Also, our proposed methodology requires minimal data and it is easily deployable on any computer.
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Raul, N., D’mello, R., Bhalerao, M. (2019). Keystroke Dynamics Authentication Using Small Datasets. In: Nandi, S., Jinwala, D., Singh, V., Laxmi, V., Gaur, M., Faruki, P. (eds) Security and Privacy. ISEA-ISAP 2019. Communications in Computer and Information Science, vol 939. Springer, Singapore. https://doi.org/10.1007/978-981-13-7561-3_7
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DOI: https://doi.org/10.1007/978-981-13-7561-3_7
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