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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-48965-0_47
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