Abstract
In this paper we investigate the problem of user authentication based on keystroke timing pattern. We propose a simple, robust and non parameterized nearest neighbor regression based feature ranking algorithm for anomaly detection. Our approach successfully handle drawbacks like outlier detection, scale variation and prevents overfitting. Apart from using existing keystroke timing features from the dataset like dwell time and flight time, other features namely bigram time and inversion ratio time are engineered as well. The efficiency and effectiveness of our method is demonstrated through extensive comparisons with other state-of-the-art techniques using CMU keystroke dynamics benchmark dataset and has shown great results in terms of average equal error rate (EER) than other proposed techniques. We achieved an average equal error rate of 0.051 for the user authentication task.
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Acknowledgement
This work was supported by http://metabolomics.iiit.ac.in/ and we would like to thank them for their support.
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Maheshwary, S., Pudi, V. (2017). Mining Keystroke Timing Pattern for User Authentication. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_14
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DOI: https://doi.org/10.1007/978-3-319-61461-8_14
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