User Authentication from Mouse Movement Data Using SVM Classifier

• Published in: Cryptology and Network Security pp. 692 - 700
• Main Authors: Anima, Bashira Akter, Jasim, Mahmood, Rahman, Khandaker Abir, Rulapaugh, Adam, Hasanuzzaman, Md
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Biometric; Cyber behavioral biometrics; Mouse dynamics; Person identification; SVM
• ISBN: 9783319489643; 331948964X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-48965-0_47

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.