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Towards Keystroke Continuous Authentication Using Time Series Analytics

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Research and Development in Intelligent Systems XXXIII (SGAI 2016)

Abstract

An approach to Keystroke Continuous Authentication (KCA) is described founded on a time series analysis based approach that, unlike previous work on KCA (using feature vector representations), takes the sequencing of keystrokes into consideration. The significance of KCA is in the context of online assessments and examinations used in eLearning environments and MOOCs, which are becoming increasingly popular. The process is fully described and analysed, including comparison with established feature vector approaches. Our proposed method outperforms these other approaches to KCA (with a detection accuracy of 94 %, compared to 79.53 %), a clear indicator that the proposed time series analysis based KCA has significant potential.

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Notes

  1. 1.

    http://www.turnitinuk.com/.

  2. 2.

    The interface can be found at: http://cgi.csc.liv.ac.uk/~hsaalshe/WBKTR3.html.

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Acknowledgments

We would like to express our thanks to those who participated in collecting the data and to Laureate Online Education b.v. for their support.

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Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Abdullah Alshehri, Frans Coenen & Danushka Bollegala

Authors
  1. Abdullah Alshehri
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  2. Frans Coenen
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  3. Danushka Bollegala
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Corresponding author

Correspondence to Abdullah Alshehri .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, East Sussex, United Kingdom

    Miltos Petridis

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Alshehri, A., Coenen, F., Bollegala, D. (2016). Towards Keystroke Continuous Authentication Using Time Series Analytics. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-47175-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47174-7

  • Online ISBN: 978-3-319-47175-4

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