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Behavior rhythm: A new model for behavior visualization and its application in system security management

He, Chao; Liu, Zhaoli; Guan, Xiaohong; Li, Shancang; Qin, Tao

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Authors

Chao He

Zhaoli Liu

Xiaohong Guan

Shancang Li Shancang.Li@uwe.ac.uk
Senior Lecturer in Computer Forensics and Security

Tao Qin



Abstract

© 2018 IEEE. The widespread use of social media, cloud computing, and Internet of Things generates massive behavior data recorded by system logs, and how to utilize these data to improve the stability and security of these systems becomes more and more difficult due to the increasing number of users and amount of data. In this paper, we propose a novel model named behavior rhythm (BR) to characterize and visualize the user's behaviors from the massive logs and apply it to the system security management. Based on the BR model, we conduct the clustering analysis to mine the user clusters. Different management and access control policies can be applied to different clusters to improve the management efficiency. Then, we apply the non-negative matrix factorization method to analyze the BRs and perform abnormal detection, and employ the BR similarity calculation to perform fast potential anomaly tracking. The detection and tracing results can help the administrators to control the threats efficiently. Experimental results based on the datasets collected from the campus network center of Xi'an Jiaotong University verify the accuracy and efficiency of our method in user behavior profiling and security management, which lay a solid foundation for improving system stability and quality of service.

Journal Article Type Article
Publication Date Nov 22, 2018
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 6
Pages 73940-73951
APA6 Citation He, C., Liu, Z., Guan, X., Li, S., & Qin, T. (2018). Behavior rhythm: A new model for behavior visualization and its application in system security management. IEEE Access, 6, 73940-73951. https://doi.org/10.1109/ACCESS.2018.2882812
DOI https://doi.org/10.1109/ACCESS.2018.2882812
Publisher URL https://doi.org/10.1109/ACCESS.2018.2882812
Additional Information Additional Information : (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

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