[1]吴小燕,刘强,朱成璋.社交网络中协同舆论欺诈检测方法应用研究[J].郑州大学学报(工学版),2022,43(02):7-14.[doi:10.13705/j.issn.1671-6833.2022.02.010]
　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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社交网络中协同舆论欺诈检测方法应用研究()

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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

43

期数:

2022年02期

页码:

7-14

栏目:

出版日期:

2022-02-27

文章信息/Info

Title:

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

作者:

吴小燕; 刘强; 朱成璋

国防科技大学计算机学院;军事科学院战争研究院;

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

关键词:

欺诈检测; 协同欺诈检测; 无监督欺诈检测; 行为识别; 社交网络安全

Keywords:

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

分类号:

TP393

DOI:

10.13705/j.issn.1671-6833.2022.02.010

文献标志码:

A

摘要:

为了使网络空间能够提供更加可靠的信息，欺诈检测变得越来越重要，但是，随着舆论欺诈 者投入更多资源，劫持正常用户、购买假用户账户等，欺诈攻击变得越来越不易分辨，其检测也越 来越具有挑战性．现有的方法使用了大量数据却忽视了真正检测需要且有效的信息，从而导致准确 性降低．因此，本文整合现有的舆论欺诈方法并对其中算法进行优化，结合基于密度子图的聚类算 法和决策树分类算法，得到检测结果较为良好的算法．本文引入了一种新的度量“对比可疑度”，该 度量主要包括拓扑连接的信息，以更为聚合的方式检测欺诈群体．该度量强调了欺诈者和正常用户 的动态对比，使得算法能够在关于拓扑连接、时间戳以及评分方面信息有效检测到欺诈者的异常行 为．此外，本文算法整体框架简洁，在基于行为的欺诈检测及异构欺诈者群体检测算法上时间复杂 度呈线性，具有较高的可扩展性．基于 Yelp 数据集的实验结果表明，本文所提出的欺诈舆论检测算 法具有较高的准确度．

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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