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Improving Implementation of Keystroke Dynamics Using K-NN and Manhattan Distance

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Big Data, Cloud and Applications (BDCA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 872))

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Abstract

Keystroke dynamics is a heavy field for researches; a lot of solutions have been proposed in this domain using different implementations usually based on Euclidean distance for measuring similarity between features vectors. However, the Euclidean distance method has a higher error equal rate comparing with other classification methods which makes the method less effective. Therefore, in the following paper, we propose our version of keystroke dynamics implementation based on K-NN, F-NN and Manhattan distance as classifiers to improve the authentication efficiency. The flight times and dwell time between keys are used in this study.

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Correspondence to Farida Jaha .

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Jaha, F., Kartit, A. (2018). Improving Implementation of Keystroke Dynamics Using K-NN and Manhattan Distance. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds) Big Data, Cloud and Applications. BDCA 2018. Communications in Computer and Information Science, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-319-96292-4_28

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  • DOI: https://doi.org/10.1007/978-3-319-96292-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96291-7

  • Online ISBN: 978-3-319-96292-4

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