User Authentication Through Mouse Dynamics

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013

User Authentication Through Mouse Dynamics
Chao Shen, Student Member, IEEE, Zhongmin Cai, Member, IEEE, Xiaohong Guan, Fellow, IEEE, Youtian Du, Member, IEEE, and Roy A. Maxion, Fellow, IEEE

Abstract—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. Index Terms—Biometric, mouse dynamics, authentication, eigenspace transformation, one-class learning.

I. INTRODUCTION

T

HE quest for a reliable and convenient security mechanism to authenticate a computer user has existed since the inadequacy of conventional password mechanism was realized, first by the security community, and then gradually by the Manuscript received March 28, 2012; revised July 16, 2012; accepted September 06, 2012. Date of publication October 09, 2012; date of current version December 26, 2012. This work was supported in part by the NSFC (61175039, 61103240, 60921003, 60905018), in part by the National Science Fund for Distinguished Young Scholars (60825202), in part by 863 High Tech Development Plan (2007AA01Z464), in part by the Research Fund for Doctoral Program of Higher Education of China (20090201120032), and in part by Fundamental Research Funds for Central Universities (2012jdhz08). The work of R. A. Maxion was supported by the National Science Foundation under Grant CNS-0716677. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors, and do not necessarily reflect the views of the National Science Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sviatoslav Voloshynovskiy. C. Shen, Z. Cai, X. Guan, and Y. Du are with the MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China (e-mail: cshen@sei.xjtu.edu.cn; zmcai@sei.xjtu.edn.cn; xhguan@sei.xjtu.edu.cn; ytdu@sei.xjtu.edu.cn). R. A. Maxion is with the Dependable Systems Laboratory, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: maxion@cs.cmu.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIFS.2012.2223677

public [31]. As data are moved from traditional localized computing environments to the new Cloud Computing paradigm (e.g., Box.net and Dropbox), the need for better authentication has become more pressing. Recently, several large-scale password leakages exposed users to an unprecedented risk of disclosure and abuse of their information [47], [48]....
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