Continuous Authentication for Mouse Dynamics a Pattern growth Approach 2012

Abstract

The aim of this work was to investigate existing algorithms for dynamic user authentication and develop our own, based on an analysis of computer mouse handling characterized by high-quality performance and a supporting dynamic mode. Existing ways of constructing and preliminarily processing the feature space are considered, along with means of dynamic authentication based on the use of classical techniques of machine learning and neural networks. Modifications with better efficiency are proposed for the ones most promising. A cross-platform application for dynamic user authentication based on an analysis of computer mouse handling is designed and implemented using the proposed technique, which shows the best performance (ROC AUC = 0.82). This system and its individual modules can serve as a basis for building advanced information security systems. Experimental studies are conducted that confirm the validity of the obtained results and the correctness of the system's operation.

REFERENCES

1. A. K. Jain, A. Ross, and S. Prabhakar, ''An introduction to biometric recognition,'' IEEE Trans. Circuits Syst. Video Technol. 14 (1), 4–20 (2004).

   Article  Google Scholar

2. J. Wayman, A. Jain, D. Maltoni, and D. Maio, ''An introduction to biometric authentication systems,'' in Biometric Systems, Ed. by J. Wayman et al. (Springer, London, 2005), pp. 1–20.

   Book  Google Scholar

3. Y. X. M. Tan, A. Binder, and A. Roy, ''Insights from curve fitting models in mouse dynamics authentication systems,'' in Proc. 2017 IEEE Conference on Application, Information and Network Security (AINS) (Miri, Malaysia, 2017), pp. 42–47.

4. A. A. Khalifa, M. A. Hassan et al., ''Comparison between mixed binary classification and voting technique for active user authentication using mouse dynamics,'' in Proc. 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE) (Khartoum, Sudan, 2015), pp. 281–286.

5. P. Chong, Y. Elovici, and A. Binder, ''User authentication based on mouse dynamics using deep neural networks: A comprehensive study,'' IEEE Trans. Inf. Forensics Secur. 15, 1086–1101 (2019).

   Article  Google Scholar

6. P. Chong, Y. X. M. Tan et al., ''Mouse authentication without the temporal aspect – What does a 2D-CNN learn?'' in Proc. 2018 IEEE Security and Privacy Workshops (SPW 2018) (San Francisco, CA, 2018), pp. 15–21.

7. M. Antal and N. Fejér, ''Mouse dynamics based user recognition using deep learning,'' Acta Univ. Sapientiae, Inf. 12 (1), 39–50 (2020).

   Google Scholar

8. S. Mondal and P. Bours, ''A study on continuous authentication using a combination of keystroke and mouse biometrics,'' Neurocomputing 230, 1–22 (2017).

   Article  Google Scholar

9. C. Feher, Y. Elovici et al., ''User identity verification via mouse dynamics,'' Inf. Sci.: Int. J. 201, 19–36 (2012).

   Article  Google Scholar

10. J. H. Friedman, ''Greedy function approximation: A gradient boosting machine,'' Ann. Stat. 29 (5), 1189–1232 (2001).

    MathSciNet  Article  Google Scholar

11. M. Kazachuk, A. Kovalchuk, I. Mashechkin, I. Orpanen, M. Petrovskiy, I. Popov, and R. Zakliakov, ''One-class models for continuous authentication based on keystroke dynamics,'' in Intelligent Data Engineering and Automated Learning – IDEAL 2016, Proc. 17th Int. Conference, Ed. by H. Yin et al., Lecture Notes in Computer Science, Vol. 9937 (Springer, Cham, 2016), pp. 416–425.

12. M. M. Breunig, H.-P. Kriegel et al., ''LOF: identifying density-based local outliers,'' ACM SIGMOD Record 29 (2), 93–104 (2000).

    Article  Google Scholar

13. F. T. Liu, K. M. Ting, and Z.-H. Zhou, ''Isolation forest,'' in Proc. Eighth IEEE International Conference on Data Mining (ICDM 2008) (Pisa, Italy, 2008), pp. 413–422.

14. B. Hoyle, M. M. Rau et al., ''Anomaly detection for machine learning redshifts applied to SDSS galaxies,'' Mon. Not. R. Astron. Soc. 452 (4), 4183–4194 (2015).

    Article  Google Scholar

15. M. Antal and L. Denes-Fazakas, ''User verification based on mouse dynamics: A comparison of public data sets,'' in Proc. 2019 IEEE 13th International Symposium on Applied Computational Intelligence and Informatics (SACI) (Timisoara, Romania, 2019), pp. 143–148.

16. C. Shen, Z. Cai, and X. Guan, ''Continuous authentication for mouse dynamics: A pattern-growth approach,'' in Proc. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2012) (Boston, MA, 2012), pp. 1–12.

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Authors and Affiliations

1. Faculty of Computational Mathematics and Cybernetics, Moscow State University, 119991, Moscow, Russia

   A. V. Berezniker, M. A. Kazachuk, I. V. Mashechkin, M. I. Petrovskiy & I. S. Popov

Corresponding authors

Correspondence to A. V. Berezniker, M. A. Kazachuk, I. V. Mashechkin, M. I. Petrovskiy or I. S. Popov.

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Translated by O. Pismenov

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Berezniker, A.V., Kazachuk, M.A., Mashechkin, I.V. et al. User Behavior Authentication Based on Computer Mouse Dynamics. MoscowUniv.Comput.Math.Cybern. 45, 135–147 (2021). https://doi.org/10.3103/S027864192104004X

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• Received: 09 August 2021

• Revised: 31 August 2021

• Accepted: 31 August 2021

• Published: 04 January 2022

• Issue Date: October 2021

• DOI : https://doi.org/10.3103/S027864192104004X

Keywords:

• dynamic authentication
• biometrics
• single-class classification
• gradient boosting
• dynamic local outlier removal
• single-class support vector technique
• neural networks
• dynamic trust level change

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Source: https://link.springer.com/article/10.3103/S027864192104004X

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