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International Conference on Big Data, Cloud and Applications

BDCA 2018: Big Data, Cloud and Applications pp 356–366Cite as

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

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

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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Author information

Authors and Affiliations

  1. Laboratory LTI, Department TRI, Chouaïb Doukkali University, ENSAJ Avenue Jabran Khalil Jabran, BP 299, El Jadida, Morocco

    Farida Jaha & Ali Kartit

Authors
  1. Farida Jaha
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  2. Ali Kartit
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Corresponding author

Correspondence to Farida Jaha .

Editor information

Editors and Affiliations

  1. Abdelmalek Essaâdi University, Tétouan, Morocco

    Youness Tabii

  2. Abdelmalek Essaâdi University, Tétouan, Morocco

    Mohamed Lazaar

  3. Abdelmalek Essaâdi University, Tétouan, Morocco

    Mohammed Al Achhab

  4. Université Ibn-Tofail, Tétouan, Morocco

    Nourddine Enneya

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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