MouseIdentity: Modeling Mouse-Interaction Behavior for a User Verification System

• Published in: IEEE transactions on human-machine systems Vol. 46; no. 5; pp. 734 - 748
• Main Authors: Chao Shen, Zhongmin Cai, Xiaomei Liu, Xiaohong Guan, Maxion, Roy A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authentication; Dynamics; Feature extraction; Human–computer interaction; Measurement; Mice; Motion segmentation; mouse-interaction behavior; one-class learning; Trajectory; user verification system
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2016.2558623

Analysis of mouse-interaction behaviors for identifying individual computer users has experienced growing interest from information security and biometric researchers. This paper presents a simple and efficient user verification system by modeling mouse-interaction behavior, which is accurate and competent for future deployment. For each mouse-operation sample, holistic attributes of mouse trajectories are first analyzed using a power transformation method, to derive a schematic representation of behavior eigenspace. Then, a propagation-based segmentation method is developed to model the detailed dynamic process of mouse movements by performing adaptive behavior segmentation and then characterizing each obtained segment using fine-grained procedural motion metrics. Both schematic and procedural cues from mouse-interaction behaviors may be used independently for verification, being fused at the decision level using combination rules. Analyses are conducted using data from 106 subjects with 21 200 mouse-operation samples. The verification system achieves a 1.96% false-rejection rate and a 1.18% false-acceptance rate with a short verification time (about 6 s) and lightweight system overload. Additional experiments on the effect of sample length and subject pool further examine the applicability of our verification system. We also compare the proposed approach with the state-of-the-art approaches for the data collected. Our findings suggest that mouse-interaction behaviors can enhance traditional authentication systems.