|Table of Contents|

Three-way identity authentication method based on mouse behavior(PDF)


Research Field:
Publishing date:


Three-way identity authentication method based on mouse behavior
Hu JunMa Kang
Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Postsand Telecommunications,Chongqing 400065,China
three-way decision identity authentication mouse behavior support vector machines K-nearest neighbor random forest
In order to achieve the balance between authentication time and authentication accuracy when using mouse behavior data for authentication,this paper proposes a new authentication method by introducing the idea of three-way decision. Firstly,this method makes the first judgment on users based on some mouse behaviors,and divides users into three categories:legal users,illegal users,and delayed decision users,so as to realize the predetermination of some users. Secondly,some more mouse behaviors are collected to judge the legitimacy of the delayed decision users,so as to ensure the authentication accuracy of the proposed method. Experimental results show that this method can effectively reduce the expected authentication time and achieve a high authentication accuracy.


[1] 荆继武. 网络可信身份管理的现状与趋势[J]. 信息安全研究,2016,2(7):666-668.
Jing Jiwu. The development status and tendency of internet trusted identity management[J]. Journal of Information Security Research,2016,2(7):666-668.
[2]沈超,蔡忠闽,管晓宏,等. 基于鼠标行为特征的用户身份认证与监控[J]. 通信学报,2010,31(7):11-75.
Shen Chao,Cai Zhongmin,Guan Xiaohong,et al. User authentication and monitoring based on mouse behavioral features[J]. Journal on Communications,2010,31(7):68-75.
[3]Matyas V J,Riha Z. Toward reliable user authentication through biometrics[J]. Security & Privacy IEEE,2003,1(3):45-49.
[4]Ahmed A A E,Traore I. A new biometric technology based on mouse dynamics[J]. IEEE Transactions on Dependable & Secure Computing,2007,4(3):165-179.
[5]Ahmed A A E,Traore I. Detecting computer intrusions using behavioral biometrics[C]//3rd Annual Conference on Privacy,Security and Trust. St. Andrews,Canada:IEEE,2005:91-98.
[6]Shen Cao,Cai Zhongmin,Guan Xiaohong,et al. User authentication through mouse dynamics[J]. IEEE Transactions on Information Forensics & Security,2013,8(1):16-30.
[7]Zheng N,Paloski A,Wang H. An efficient user verification system using angle-based mouse movement biometrics[J]. ACM Transactions on Information and System Security,2016,18(3):1-27.
[8]Zheng N,Paloski A,Wang H. An efficient user verification system via mouse movements[C]//Proceedings of the 18th ACM conference on Computer and communications security. Chicago,USA:ACM,2011:139-150.
[9]徐剑,李明洁,周福才,等. 基于用户鼠标行为的身份认证方法[J]. 计算机科学,2016,43(2):148-154.
Xu Jian,Li Mingjie,Zhou Fucai,et al. Identity authentication method based on user’s mouse behavior[J]. Computer Science,2016,43(2):148-154.
[10]Mondal S,Bours P. Continuous authentication using mouse dynamics[C]//2013 International Conference of the Biometrics Special Interest Group(BIOSIG). Darmstadt,Germany:IEEE,2013:1-12.
[11]Yao Yiyu. Three-way decision:an interpretation of rules in rough set theory[C]//International Conference on Rough Sets and Knowledge Technology. Berlin,Germany:Springer,2009:642-649.
[12]Yao Yiyu. Three-way decisions with probabilistic rough sets[J]. Information Sciences,2010,180(3):341-353.
[13]Yao Yiyu. Three-way decisions and cognitive computing[J]. Cognitive Computation,2016,8(4):543-554.
[14]Wang P,Yao Y. CE3:a three-way clustering method based on mathematical morphology[J]. Knowledge-Based Systems,2018,155:54-65.
[15]梁德翠,曹雯. 三支决策模型及其研究现状分析[J]. 电子科技大学学报(社科版),2019,21(1):104-112.
Liang Decui,Cao Wen. Three-way decisions:model and the state of the art[J]. Journal of UESTC(Social Sciences Edition),2019,21(1):104-112.
[16]Huang J,Wang J,Yao Y,et al. Cost-sensitive three-way recommendations by learning pair-wise preferences[J]. International Journal of Approximate Reasoning,2017,86:28-40.
[17]Zhang H R,Min F. Three-way recommender systems based on random forests[J]. Knowledge-Based Systems,2016,91:275-286.
[18]Yu H. Three-way decisions and three-way clustering[C]//International Joint Conference on Rough Sets. Cham:Springer,2018:13-28.
[19]Yu H,Liu Z,Wang G. An automatic method to determine the number of clusters using decision-theoretic rough set[J]. International Journal of Approximate Reasoning,2014,55(1):101-115.
[20]张越兵,苗夺谦,张志飞. 基于三支决策的多粒度文本情感分类模型[J]. 计算机科学,2017,44(12):188-193.
Zhang Yuebing,Miao Duoqian,Zhang Zhifei. Multi-granularity text sentiment classification model based on three-way decisions[J]. Computer Science,2017,44(12):188-193.
[21]Li H,Zhou X. Risk decision making based on decision-theoretic rough set:a three-way view decision model[J]. International Journal of Computational Intelligence Systems,2011,4(1):1-11.
[22]Li W,Miao D,Wang W,et al. Hierarchical rough decision theoretic framework for text classification[C]//9th IEEE International Conference on Cognitive Informatics(ICCI’10). Washington DC,USA:IEEE,2010:484-489.
[23]Zhou B,Yao Y,Luo J. A three-way decision approach to email spam filtering[C]//Canadian Conference on Artificial Intelligence. Berlin,Germany:Springer,2010:28-39.
[24]王宇,杨志荣,杨习贝. 决策粗糙集属性约简:一种局部视角方法[J]. 南京理工大学学报,2016,40(4):444-449.
Wang Yu,Yang Zhirong,Yang Xibei. Local attribute reduction approach based on decision-theoretic rough set[J]. Journal of Nanjing University of Science and Technology,2016,40(4):444-449.
[25]Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods[J]. Advances in large margin classifiers,1999,10(3):61-74.
[26]Fül?p á,Kovács L,Kurics T. Windhager-Pokol,E.(2016). Balabit Mouse Dynamics Challenge data set. Available at:https://github.com/balabit/Mouse-Dynamics-Challenge.


Last Update: 2019-09-30