Abstract
This paper presents a robust user authentication system by gleaning raw mouse movement data. The data was collected using a publicly available tool called Recording User Input (RUI) from 23 subjects analyzed for three types of mouse actions - Mouse Move, Point-and-Click on Left or Right mouse button, and Drag-and-Drop. Samples are broken down to unit blocks comprising a certain number of actions and from each block seventy-four features are extracted to construct feature vectors. The proposed system was rigorously tested against public benchmark data. Experiment results generated by using the Support Vector Machine (SVM) classifier shows a False Rejection Rate (FRR) of 1.1594 % and a False Acceptance Rate (FAR) of 1.9053 % when the block size was set for 600 actions. After reducing dimensions using Principle Component Analysis (PCA), SVM classifier shows FRR of 1.2081 % and FAR of 2.3604 %. Compared with the existing methods based on mouse movements, our method shows significantly lower error rates, which we opine are viable enough to become an alternate to conventional authentication systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jain, A.K., Pankanti, S.: Biometric identification. Commun. ACM 43, 91–98 (2000)
Kukreja, U., Stevenson, W.E., Ritter, F.E.: RUI: recording user input from interfaces under Windows and Mac OS X. Behav. Res. Methods 38(4), 656–659 (2011)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods. Cambridge University Press, New York (2000)
Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011). Article No. 27
Nakkabi, Y., Traoré, I., Ahmed, A.A.E.: Improving mouse dynamics biometric performance using variance reduction via extractors with separate features. Trans. Sys. Man Cyber. Part A 40(6), 1345–1353 (2010)
Ahmed, A.A.E., Traoré, I.: A new biometric technology based on mouse dynamics. IEEE Trans. Dependable Sec. Comput. 4(3), 165–179 (2007)
Pusara, M., Brodley, C.E.: User re-authentication via mouse movements. In: ACM Workshop on Visualization and Data Mining for Computer Security. ACM Press (2004)
Muthumari, R.S., Pepsi, M.B.B.: Mouse gesture based authentication using machine learning algorithm. In: International Conference on Advanced Communication Control and Computing Technologies (2014)
Muthumari, G., Shenbagaraj, R., Pepsi, M.B.B.: Authentication of user based on mouse-behavior data using classification. In: IEEE International Conference on Innovations in Engineering and Technology (ICIETŠ) (2014)
Lakshmipriya, D., Balakrishnan, J.R.: Holistic and procedural features for authenticating users 16, 98–101 (2014)
Rahman, K.A., Moormann, R., Dierich, D., Hossain, M.: Continuous user verification via mouse activities. In: Dziech, A., et al. (eds.) MCSS 2015. CCIS, vol. 566, pp. 170–181. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26404-2_14
Acknowledgements
This work was done under the assistance of Ministry of Posts, Telecommunications and Information Technology Fellowship given by the Information and Communication Technology division of Ministry of Posts, Telecommunications and Information Technology, Government of the People’s Republic of Bangladesh.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Anima, B.A., Jasim, M., Rahman, K.A., Rulapaugh, A., Hasanuzzaman, M. (2016). User Authentication from Mouse Movement Data Using SVM Classifier. In: Foresti, S., Persiano, G. (eds) Cryptology and Network Security. CANS 2016. Lecture Notes in Computer Science(), vol 10052. Springer, Cham. https://doi.org/10.1007/978-3-319-48965-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-48965-0_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48964-3
Online ISBN: 978-3-319-48965-0
eBook Packages: Computer ScienceComputer Science (R0)