Accuracy Comparison of Classification Techniques for Mouse Dynamics-Based Biometric CaRP

  • Sushama KulkarniEmail author
  • Hanmant Fadewar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)


Combining more than one authentication schemes enhances the robustness of a system against cyber-attacks. In this paper, we introduce a novel mouse dynamics-based biometric CaRP (CAPTCHA as gRaphical Password). It combines mouse dynamics-based biometric authentication scheme with knowledge-based authentication scheme. This study primarily focuses on the comparison of classification accuracy of binary decision tree, SVM, and ANN for proposed mouse dynamics-based authentication scheme of CaRP.


Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) Web security Binary decision tree Support Vector Machines (SVM) Artificial Neural Network (ANN) Mouse dynamics CAPTCHA as gRaphical passwords (CaRP) 



We wish to acknowledge volunteer participants of this study who took time to handle the proposed system and provided their permission to collect mouse dynamics data.


  1. 1.
    Jermyn, I., A. Mayer, F. Monrose, M. Reiter, K. K., and A.D. Rubin. 1999. The Design and Analysis of Graphical Passwords. In: Proceedings of the 8th USENIX Security Symposium.Google Scholar
  2. 2.
    Zhu, B.B., J. Yan, G. Bao, M. Yang, and N. Xu. 2014. Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems. IEEE Transactions on Information Forensics and Security 9 (6): 891–904.CrossRefGoogle Scholar
  3. 3.
    Real User Corporation. 2005. How the Passface System Works.Google Scholar
  4. 4.
    Dhamija, R., and A. Perrig. 2000. Déjà Vu: A User Study Using Images for Authentication. In Proceedings of the 9th conference on USENIX Security Symposium.Google Scholar
  5. 5.
    Varenhorst, C. 2004. Passdoodles: A Lightweight Authentication Method. MIT Research Science Institute.Google Scholar
  6. 6.
    Blonder, G. 1996. Graphical Passwords. U.S. Patent 5559961.Google Scholar
  7. 7.
    Wiedenbeck, S., J. Waters, J.C. Birget, A. Brodskiy, and N. Memon. 2005. PassPoints: Design and Longitudinal Evaluation of a Graphical Password System. International Journal of Human-Computer Studies (Special Issue on HCI Research in Privacy and Security) 63: 102–127.Google Scholar
  8. 8.
    Chiasson, S., A. Forget, R. Biddle, and P.C. van Oorschot. 2008. Influencing Users Towards Better Passwords: Persuasive Cued Clickpoints. In Proceedings of HCI, 121–130. Liverpool, UK: British Computer Society.Google Scholar
  9. 9.
    Jorgensen, Z., and T. Yu. 2011. On Mouse Dynamics As a Behavioral Biometric for Authentication. In Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, ASIACCS 2011, 476–482. New York: ACM.Google Scholar
  10. 10.
    Traore, I., I. Woungang, M.S. Obaidat, Y. Nakkabi, and I. Lai. 2012. Combining Mouse and Keystroke Dynamics Biometrics for Risk-Based Authentication in Web Environments. In Fourth International Conference on Digital Home, 138–145. Guangzhou, China: IEEE.Google Scholar
  11. 11.
    Nakkabi, Y., I. Traore, and A.A.E. Ahmed. 2010. Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 40 (6): 1345–1353.CrossRefGoogle Scholar
  12. 12.
    Mondal, S., and P. Bours. 2013. Continuous Authentication Using Mouse Dynamics. In International Conference of the BIOSIG Special Interest Group (BIOSIG) 1–12, Darmstadt.Google Scholar
  13. 13.
    Monroe, D. 2012. Biometrics Metrics Report v3.0.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computational SciencesS. R. T. M. UniversityNandedIndia

Personalised recommendations