Skip to main content
SpringerLink
Log in
Book cover

International Conference on Human-Computer Interaction

HCII 2020: HCI International 2020 - Posters pp 68–75Cite as

Assessing User Behavior by Mouse Movements

Part of the Communications in Computer and Information Science book series (CCIS,volume 1224)

Abstract

In this working paper, we study user identification via mouse movement. Instead of treating the problem as a multi-class classification task, we cast user identification as a one-class problem and propose to learn an individual model for every user. Preliminary empirical results show that our approach works for some but not all users. We report on lessons learned.

Keywords

  • Mouse movement
  • User identification
  • User behavior

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://github.com/balabit/Mouse-Dynamics-Challenge.

References

  1. Antal, M., Egyed-Zsigmond, E.: Intrusion detection using mouse dynamics. IET Biometrics 8, 285–294 (2019)

    CrossRef  Google Scholar 

  2. Arapakis, I., Lalmas, M., Valkanas, G.: Understanding within-content engagement through pattern analysis of mouse gestures. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 1439–1448. ACM (2014)

    Google Scholar 

  3. Arapakis, I., Leiva, L.A.: Predicting user engagement with direct displays using mouse cursor information. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Inform. Retrieval, pp. 599–608. ACM (2016)

    Google Scholar 

  4. Chong, P., Elovici, Y., Binder, A.: User authentication based on mouse dynamics using deep neural networks: a comprehensive study. IEEE Trans. Inf. Forensics Secur. 15, 1086–1101 (2020)

    CrossRef  Google Scholar 

  5. Feher, C., Elovici, Y., Moskovitch, R., Rokach, L., Schclar, A.: User identity verification via mouse dynamics. Inf. Sci. 201, 19–36 (2012)

    CrossRef  Google Scholar 

  6. Gamboa, H., Fred, A.: A behavioral biometric system based on human-computer interaction. In: Jain, A.K., Ratha, N.K. (eds.) Biometric Technology for Human Identification, vol. 5404, pp. 381–392. SPIE (2004)

    Google Scholar 

  7. Kloft, M., Brefeld, U., Sonnenburg, S., Zien, A.: Lp-norm multiple kernel learning. J. Mach. Learn. Res. 12, 953–997 (2011)

    MathSciNet  MATH  Google Scholar 

  8. Lagun, D., Ageev, M., Guo, Q., Agichtein, E.: Discovering common motifs in cursor movement data for improving web search. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 183–192. ACM (2014)

    Google Scholar 

  9. McNeill, D.: Hand and Mind: What Gestures Reveal About Thought. University of Chicago Press, Chicago (1992)

    Google Scholar 

  10. Mueller, F., Lockerd, A.: Cheese: tracking mouse movement activity on websites, a tool for user modeling. In: CHI 2001 extended abstracts on Human factors, April 2001

    Google Scholar 

  11. Navalpakkam, V., Churchill, E.: Mouse tracking: measuring and predicting users’ experience of web-based content. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems - CHI 2012 (2012)

    Google Scholar 

  12. Ruff, L., et al.: Deep one-class classification. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, 10–15 July 2018, vol. 80, pp. 4393–4402. PMLR (2018)

    Google Scholar 

  13. Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. In: Neural Computing, vol. 13, p. 1443–1471. MIT Press, Cambridge, MA, USA (Jul 2001)

    Google Scholar 

  14. Shen, C., Cai, Z., Guan, X., Du, Y., Maxion, R.A.: User authentication through mouse dynamics. IEEE Trans. Inf. Forensics Secur. 8, 16–30 (2013)

    CrossRef  Google Scholar 

  15. Tax, D., Duin, R.: Support vector data description. Mach. Learn. 54, 45–66 (2004). https://doi.org/10.1023/B:MACH.0000008084.60811.49

    CrossRef  MATH  Google Scholar 

  16. Tzafilkou, K., Protogeros, N.: Mouse behavioral patterns and keystroke dynamics in end-user development: what can they tell us about users’ behavioral attributes? Comput. Hum. Behav. 83, 288–305 (2018)

    CrossRef  Google Scholar 

  17. Zimmermann, P., Guttormsen, S., Danuser, B., Gomez, P.: Affective computing - measuring mood with mouse and keyboard. Int. J. Occup. Saf. Ergon. 9, 539–51 (2003)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Learning Group, Leuphana University of Lüneburg, Lüneburg, Germany

    Jennifer Jorina Matthiesen & Ulf Brefeld

Authors
  1. Jennifer Jorina Matthiesen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ulf Brefeld
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jennifer Jorina Matthiesen .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Prof. Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Dr. Margherita Antona

A Additional Features

A Additional Features

Table 2. List of additional features
Full size table

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Matthiesen, J.J., Brefeld, U. (2020). Assessing User Behavior by Mouse Movements. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1224. Springer, Cham. https://doi.org/10.1007/978-3-030-50726-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50726-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50725-1

  • Online ISBN: 978-3-030-50726-8

  • eBook Packages: Computer ScienceComputer Science (R0)

search

Navigation