User Authentication Through Mouse Dynamics

• Published in: IEEE transactions on information forensics and security Vol. 8; no. 1; pp. 16 - 30
• Main Authors: Chao Shen, Zhongmin Cai, Xiaohong Guan, Youtian Du, Maxion, R. A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Authentication; Behavior; Biometric; Biometrics; Biometrics (access control); Computer information security; Computer peripherals; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Dynamics; Effectiveness; eigenspace transformation; Exact sciences and technology; Feature extraction; Learning; Learning (artificial intelligence); Memory and file management (including protection and security); Memory organisation. Data processing; mouse dynamics; one-class learning; Software; Studies; Tasks; Cluster analysis; Biometrics; Biometric; Acceptance; One-class classification; Experimental result; Behavioral analysis; User interface; Holism; Authentication; mouse dynamics; Fine grain structure; eigenspace transformation; one-class learning; Mouse (computer peripheral); Computer security; User behavior; Distance; Pattern extraction
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2012.2223677

Behavior-based user authentication with pointing devices, such as mice or touchpads, has been gaining attention. As an emerging behavioral biometric, mouse dynamics aims to address the authentication problem by verifying computer users on the basis of their mouse operating styles. This paper presents a simple and efficient user authentication approach based on a fixed mouse-operation task. For each sample of the mouse-operation task, both traditional holistic features and newly defined procedural features are extracted for accurate and fine-grained characterization of a user's unique mouse behavior. Distance-measurement and eigenspace-transformation techniques are applied to obtain feature components for efficiently representing the original mouse feature space. Then a one-class learning algorithm is employed in the distance-based feature eigenspace for the authentication task. The approach is evaluated on a dataset of 5550 mouse-operation samples from 37 subjects. Extensive experimental results are included to demonstrate the efficacy of the proposed approach, which achieves a false-acceptance rate of 8.74%, and a false-rejection rate of 7.69% with a corresponding authentication time of 11.8 seconds. Two additional experiments are provided to compare the current approach with other approaches in the literature. Our dataset is publicly available to facilitate future research.