Continuous authentication for mouse dynamics: A pattern-growth approach

Abstract

Mouse dynamics is the process of identifying individual users based on their mouse operating characteristics. Although previous work has reported some promising results, mouse dynamics is still a newly emerging technique and has not reached an acceptable level of performance. One of the major reasons is intrinsic behavioral variability. This study presents a novel approach by using pattern-growth-based mining method to extract frequent-behavior segments in obtaining stable mouse characteristics, employing one-class classification algorithms to perform the task of continuous user authentication. Experimental results show that mouse characteristics extracted from frequent-behavior segments are much more stable than those from holistic behavior, and the approach achieves a practically useful level of performance with FAR of 0.37% and FRR of 1.12%. These findings suggest that mouse dynamics suffice to be a significant enhancement for a traditional authentication system. Our dataset is publicly available to facilitate future research.