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Robustness of Keystroke Dynamics Identification Algorithms Against Brain-Wave Variations Associated with Emotional Variations

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Intelligent Systems and Applications (IntelliSys 2019)

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Abstract

Keystroke dynamics facilitates the identification of a person by the way they type. This article focuses on analysing the robustness of keystroke dynamics algorithms against variations in biometric records through electroencephalography, using waves associated with states of relaxation and excitement and a self-report questionnaire. An experiment was conducted to capture keystroke patterns in different affective states. The results suggested specific classification distances such as A and R metrics, Canberra distance and two Minkowski-based distances have their accuracy slightly and negatively influenced by changing moods. Euclidean distance seemed to be the least affected.

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Notes

  1. 1.

    The source code of the experiment can be downloaded from https://github.com/lsia/pocs.

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Acknowledgments

This paper acknowledges support from the University of Buenos Aires with the project under UBACyT 20020130200140BA and PDE-44-2019 placed in the Laboratorio de Sistemas de Información Avanzados, Engineering Faculty, University of Buenos Aires. The project recognises the collaboration from the ISIER (FICCTE-SECYT UM) assisting only in BCI application concerns, under the PID 01-001/12/14.

Author information

Authors and Affiliations

  1. Laboratorio de Sistemas de Información Avanzados, Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina

    Enrique P. Calot & Jorge S. Ierache

  2. Instituto de Sistemas Inteligentes y Enseñanza Experimental de la Robótica (ISIER), SECyT – Universidad de Morón, Morón, Argentina

    Jorge S. Ierache

  3. Instituto de Investigación en Informática (III-LIDI), Facultad de Informática, Universidad Nacional de La Plata, Investigador Asociado – Comisión de Investigaciones Científicas (CIC), La Plata, Argentina

    Waldo Hasperué

Authors
  1. Enrique P. Calot
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  3. Waldo Hasperué
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Corresponding author

Correspondence to Jorge S. Ierache .

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Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Calot, E.P., Ierache, J.S., Hasperué, W. (2020). Robustness of Keystroke Dynamics Identification Algorithms Against Brain-Wave Variations Associated with Emotional Variations. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_15

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