Research progress of abnormal user detection technology in social network

Qiang QU; Hongtao YU; Ruiyang HUANG

doi:10.11959/j.issn.2096-109x.2018025

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Research progress of abnormal user detection technology in social network

Comprehensive Reviews | 更新时间：2024-06-03

- Research progress of abnormal user detection technology in social network
- Chinese Journal of Network and Information Security Vol. 4, Issue 3, Pages: 13-23(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2018025
  CLC： T309
- Online First：2018-03，
  
  Published：15 March 2018
- 稿件说明：
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Qiang QU, Hongtao YU, Ruiyang HUANG. Research progress of abnormal user detection technology in social network[J]. Chinese Journal of Network and Information Security, 2018, 4(3): 13-23.
DOI：

Qiang QU, Hongtao YU, Ruiyang HUANG. Research progress of abnormal user detection technology in social network[J]. Chinese Journal of Network and Information Security, 2018, 4(3): 13-23. DOI： 10.11959/j.issn.2096-109x.2018025.

摘要

在社交网络中，异常用户检测问题是网络安全研究的关键问题之一，异常用户通过创建多个马甲进行虚假评论，网络欺凌或网络攻击等行为严重威胁正常用户的信息安全和社交网络的信用体系，因此大量研究人员对该问题进行了深入研究。回顾了近年来该问题的研究成果，并总结出一个整体架构。数据收集层介绍数据获取方式与相关数据集；特征表示层阐述属性特征、内容特征、网络特征、活动特征与辅助特征；算法选择层介绍监督算法、无监督算法与图算法；结果评估层阐述数据标注方式与方法评估指标。最后，展望了该领域未来的研究方向。

Abstract

In social networks

the problem of anomalous users detection is one of the key problems in network security research.The anomalous users conduct false comments

cyberbullying or cyberattacks by creating multiple vests

which seriously threaten the information security of normal users and the credit system of social networks

so a large number of researchers conducted in-depth study of the issue.The research results of the issue in recent years were reviewed and an overall structure was summarized.The data collection layer introduces the data acquisition methods and related data sets

and the feature presentation layer expounds attribute features

content features

network features

activity features and auxiliary features.The algorithm selection layer introduces supervised algorithms

unsupervised algorithms and graph algorithms.The result evaluation layer elaborates the method of data annotation method and index.Finally

the future research direction in this field was looked forward.

关键词

Keywords

references

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