Research on Application of Collaborative Public Opinion Fraud Detection Method in Social Network

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Research on Application of Collaborative Public Opinion Fraud Detection Method in Social Network

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[1]WU Xiaoyan,LIU Qiang,ZHU Chengzhang.Research on Application of Collaborative Public Opinion Fraud Detection Method in Social Network[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):7-14.[doi:10.13705/j.issn.1671-6833.2022.02.010]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 02 Page number: 7-14 Column: Public date: 2022-02-27

Title:: Research on Application of Collaborative Public Opinion Fraud Detection Method in Social Network

Author(s):: WU Xiaoyan¹; LIU Qiang¹; ZHU Chengzhang²; 1.College of Computer, National University of Defense Technology, Changsha 410005,China;
2.Institute of War,Academy of Military Sciences, Beijing 100091, China

Keywords:: fraud detection; collaborative fraud detection; unsupervised fraud detection; behavior recognition; social network security

CLC:: TP393

DOI:: 10.13705/j.issn.1671-6833.2022.02.010

Abstract:: In order to ensure cyberspace to provide more reliable information, fraud detection became more important. Existing methods only considered the static dense sub-graphs formed between user comments when detecting fraudulent users, while ignored the abnormal behavior of users during the comments, which led to reduced accuracy. Meanwhile, further manual verification was often required to verify the reliability of the results in practice. For this problem, this paper proposed the CPOFD method, which used a new measure “comparative equivocation”. This measure mainly included topological connection information to detect fraud groups in a more aggregated manner. Specifically, this metric emphasized the dynamic comparison between fraudsters and normal users, so that the algorithm could effectively detect the fraudster′s abnormal behavior in terms of topological connections, timestamps, and scoring information. At the same time, this method combined the clustering algorithm based on the density sub-graphs and the decision tree classification algorithm to group users in the social network effectively, and used the simulated annealing algorithm to optimize the pruning when classifying the clusters, so as to find the approximate optimum solution more concisely and quickly. The time complexity of the algorithm was linear to the number of fraudsters, and it had high scalability. In experiments based on the Yelp dataset, the accuracy of the CPOFD method for fraudulent public opinion detection reached more than 98%, which verified the effectiveness of the CPOFD method.

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Last Update: 2022-02-25