基于社交网络结构的马甲水军检测方法

信息安全研究 ›› 2019, Vol. 5 ›› Issue (11): 993-999.

• 内容安全与人工智能专题 • 上一篇下一篇

基于社交网络结构的马甲水军检测方法

周薇,卫玲蔚,韩冀中

中国科学院信息工程研究所

收稿日期:2019-11-08 出版日期:2019-11-15 发布日期:2019-11-20
通讯作者: 周薇
作者简介:周薇博士, 助理研究员, 主要研究方向为数据挖掘、知识图谱、异常账号挖掘. zhouwei@iie.ac.cn 卫玲蔚博士研究生, 主要研究方向为社交网络分析. weilingwei@iie.ac.cn 韩冀中博士, 正高级工程师, 主要研究方向为云计算、多媒体分析、社交网络分析. hanjizhong@iie.ac.cn

A Spammer Detection Method Based on Social Network Structure

Received:2019-11-08 Online:2019-11-15 Published:2019-11-20

摘要/Abstract

摘要： 在社交网络中,一些有害账号被检测并拦截后,又会衍生出新的马甲水军账号继续传播负面言论、谣言等,严重损害了社会公众的利益.以往许多马甲水军检测工作都是基于语言特征和非语言行为特征(如发文习惯)开展的,虽然取得了一定的成功,但一些聪明的马甲水军很容易伪造他们的语言和行为特征来逃避检测,因此很难保证这些检测方法的性能.然而,在社交网络中,用户间的社交结构并没有被充分地挖掘和利用.提出基于社交网络结构的在线马甲水军检测方法,将马甲水军识别转化为相似子图匹配问题.该方法在新浪微博数据集上进行了实验,实验结果证明了所提出的马甲水军检测方法的有效性.

关键词: 马甲水军识别, 社交网络结构, 相似子图匹配, 行为特征, 在线检测

Abstract: In the social network, after some harmful accounts are detected and intercepted, new spammers will be derived to continue to spread negative comments and rumors, which seriously damages the interests of the public. In the past, spammer detection methods were based on linguistic features and nonverbal behavioral features (such as writing habits). Although some success has been achieved, some clever puppet masters can easily disguise their language and behavioral features to evade detection. It is difficult to guarantee the performance of these detection methods. However, the social structure between users is not fully exploited and utilized in social networks. In this paper, a spammer detection method based on social network structure is proposed, to transform the identification into a similar subgraph matching problem. The method proposed in this paper is carried out on the Sina Weibo dataset, and the experimental results prove the effectiveness of the proposed spammer detection method.

Key words: spammer detection, social network structure, similar subgraph matching, behavioral feature, online detection

周薇卫玲蔚韩冀中. 基于社交网络结构的马甲水军检测方法[J]. 信息安全研究, 2019, 5(11): 993-999.

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基于社交网络结构的马甲水军检测方法

A Spammer Detection Method Based on Social Network Structure

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