Abstract
With the increasingly prominent problem of information security, the research on user trustworthy authentication technology becomes more and more important. Identification and authentication methods based on user’s biological behavior characteristics have attracted widespread attention due to their low cost and difficulty in imitation, which represented by mouse dynamics. This study proposed an improved method for time-frequency joint analysis of mouse behaviors for trustworthy authentication of website users. We collected the behavior data of the user’s natural mouse operation under real website environment, and analyzed the timing and spatial characteristics of the user’s mouse movements. Based on extracting the time-frequency joint distribution characteristics and spatial distribution characteristics of the temporal signals of the user’s mouse movements, we used the random forest algorithm to establish a user’s trustworthy authentication model. Mouse behavior data of five users during twenty-eight months had been used as a case study to explore the effectiveness of this method in user trustworthy authentication. The results of case analysis showed that, comparing to the original research, the method proposed in this study significantly improved the accuracy of the website user trustworthy authentication.
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References
Xu, J., Li, M., Zhou, F., Xue, R.: Identity authentication method based on user’s mouse behavier. Comput. Sci 43(2), 148–154 (2016)
Grother, P., Tabassi, E.: Performance of biometric quality measures. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 531–543 (2007)
Malathi, R., Jeberson, R.R.R.: An integrated approach of physical biometric authentication system. Procedia Comput. Sci. 85, 820–826 (2016)
Jain, A.K., Pankanti, S., Prabhakar, S., Hong, L., Wayman, J.L.: Biometrics: a grand challenge. In: Proceedings of ICPR, vol. 2, no. 4, pp. 935–942 (2004)
Zweig, M.H., Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39(4), 561–577 (1993)
Shrobe, H., Shrier, D.L., Pentland, A.: New Solutions for Cybersecurity. The MIT Press, Boston (2018)
Yi, S., Li, J., Yi, Q.: Trustworthy interaction detection method in view of user behavior flow diagram. Control Decis. 1–8 (2019)
Ho, J., Kang, D.-K.: One-class naive Bayes with duration feature ranking for accurate user authentication using keystroke dynamics. Appl. Intell.: Int. J. Artif. Intell. Neural Netw. Complex Problem-Solving Technol. 48(6), 1547–1564 (2018)
Shen, C., Cai, Z., Guan, X.: Continuous authentication for mouse dynamics: a pattern-growth approach. In: 42nd IEEE/IFIP International Conference on Dependable Systems & Networks, Boston, pp. 1–12. IEEE Computer Society (2012)
Pusara, M., Brodley, C.: User re-authentication via mouse movements. In: 11th Workshop on Visualization and Data Mining for Computer Security, Washington, pp. 1–8. ACM (2004)
Shen, C., Cai, Z., Liu, X., Guan, X., Maxion, R.A.: MouseIdentity: Modeling mouse-interaction behavior for a user verification system. IEEE Trans. Hum.-Mach. Syst. 46(5), 734–748 (2016)
Wang, B., Xiong, S., Yi, S., Yi, Q., Yan, F.: Measuring network user trust via mouse behavior characteristics under different emotions. In: Moallem, A. (ed.) HCII 2019. LNCS, vol. 11594, pp. 471–481. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22351-9_32
Sayed, B., Traore, I., Woungang, I., Obaidat, M.S.: Biometric authentication using mouse gesture dynamics. IEEE Syst. J. 7(2), 262–274 (2013)
Nakkabi, Y., Traore, I., Ahmed, A.A.E.: Improving mouse dynamics biometric performance using variance reduction via extractors with separate features. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 40(6), 1345–1353 (2010)
Feher, C., Elovici, Y., Moskovitch, R., Rokach, L., Schclar, A.: User identity verification via mouse dynamics. Inf. Sci. 201, 19–36 (2012)
Chen, Y.: Research and Implementation of User Identification Based on Mouse Dynamics (2018)
Zheng, N., Paloski, A., Wang, H.: An efficient user verification system using angle-based mouse movement biometrics. ACM Trans. Inf. Syst. Secur. 18(3), 1–27 (2016)
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This work was supported by the National Natural Science Foundation of China under Grant No. 71671020.
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Li, W., Yi, S., Yi, Q., Li, J., Xiong, S. (2020). An Improved Method of Time-Frequency Joint Analysis of Mouse Behavior for Website User Trustworthy Authentication. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2020. Lecture Notes in Computer Science(), vol 12210. Springer, Cham. https://doi.org/10.1007/978-3-030-50309-3_39
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DOI: https://doi.org/10.1007/978-3-030-50309-3_39
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