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Issue title: New Techniques for Intelligent Networks with Machine Learning
Guest editors: Nan Jiang, Khaled Riad and Weiwei Lin
Article type: Research Article
Authors: Xiong, Jianyinga | Liu, Haib | Liu, Chengqic; *
Affiliations: [a] Department of Technology and Information Security, Jiangxi Police College, Nanchang, China | [b] Yichun Public Security Bureau, Yichun City, China | [c] Network Center of Nanchang University, Nanchang City, China
Correspondence: [*] Corresponding author: Chengqi Liu. E-mail: [email protected].
Abstract: Social network has become an important channel for people to obtain information.Trusted user information behavior is the key to build cyberspace security. A dynamic reputation evaluation method based on supervision feedback of user information behavior is helpful to promote social network self-discipline and achieve good community autonomy. The comprehensive reputation evaluation of each node integrates identity and behavior reputation. And the reputation is dynamically updated by setting the new node evaluation period and phased update mechanism. Identity reputation is calculated by information disclosure and network characteristics; Behavior reputation is calculated by information release and forwarding, and rewards or punishments will be given to self-correction of information behavior or blocking of bad information. The simulation results show that compared with the traditional trust evaluation mechanism, setting rewards and punishments guidance can improve the accuracy of reputation evaluation. At the same time, reputation incentive can also inhibit the interaction of bad information while promoting the consciousness of reporting.
Keywords: Social network, reputation, information behavior, reputation rewards and punishments, network security
DOI: 10.3233/JHS-220683
Journal: Journal of High Speed Networks, vol. 28, no. 2, pp. 107-120, 2022
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