恶意社交机器人检测技术研究

刘蓉; 陈波; 于泠; 刘亚尚; 陈思远

doi:10.11959/j.issn.1000-436x.2017275

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恶意社交机器人检测技术研究

综述 | 更新时间：2024-06-05

- 恶意社交机器人检测技术研究
- Overview of detection techniques for malicious social bots
- 通信学报 2017年38卷第Z2期页码：197-210
- 作者机构：
  
  1. 南京师范大学计算机科学与技术学院,江苏南京 210023
  2. 江苏省大规模复杂系统数值模拟重点实验室，江苏南京 210023
- 作者简介：
  
  [ "刘蓉（1994-），女，江苏泰州人，南京师范大学硕士生，主要研究方向为信息安全、社交网络等。" ]
  [ "陈波（1972-），男，江苏南通人，南京师范大学教授、硕士生导师，主要研究方向为信息安全、社会计算等。" ]
  [ "于泠（1971-），女，江苏金坛人，南京师范大学副教授，主要研究方向为信息安全、社会计算。" ]
  [ "刘亚尚（1990-），女，河南郑州人，南京师范大学硕士生，主要研究方向为信息安全、社会计算。" ]
  [ "陈思远（1993-），男，江苏淮安人，南京师范大学硕士生，主要研究方向为信息安全、Android移动安全等。" ]
- 基金信息：
  
  赛尔网络下一代互联网技术创新基金资助项目(NGII20160509);江苏省高等教育教学改革重点课题基金资助项目(2015JSJG034)
- DOI：10.11959/j.issn.1000-436x.2017275
  中图分类号： TP391
- 网络首发：2017-11，
  
  纸质出版：2017-11-25
- 稿件说明：
移动端阅览
刘蓉, 陈波, 于泠, 等. 恶意社交机器人检测技术研究[J]. 通信学报, 2017,38(Z2):197-210.

Rong LIU, Bo CHEN, Ling YU, et al. Overview of detection techniques for malicious social bots[J]. Journal on Communications, 2017, 38(Z2): 197-210.
刘蓉, 陈波, 于泠, 等. 恶意社交机器人检测技术研究[J]. 通信学报, 2017,38(Z2):197-210. DOI： 10.11959/j.issn.1000-436x.2017275.

Rong LIU, Bo CHEN, Ling YU, et al. Overview of detection techniques for malicious social bots[J]. Journal on Communications, 2017, 38(Z2): 197-210. DOI： 10.11959/j.issn.1000-436x.2017275.

摘要

攻击者利用恶意社交机器人窃取用户隐私、传播虚假消息、影响社会舆论，严重威胁了个人信息安全、社会公共安全，乃至国家安全。攻击者还在不断引入新技术实施反检测。恶意社交机器人检测成为在线社交网络安全研究的一个重点和难点。首先回顾了当前社交机器人的开发与应用现状，接着对恶意社交机器人检测问题进行了形式化定义，并分析了检测恶意社交机器人所面临的主要挑战。针对检测特征的选取问题，厘清了从静态用户特征、动态传播特征，以及关系演化特征的研究发展思路。针对检测方法问题，从基于特征、机器学习、图论以及众包4个类别总结了已有检测方案的研究思路，并剖析了几类方法在检测准确率、计算代价等方面的局限性。最后，提出了一种基于并行优化机器学习方法的恶意社交机器人检测框架。

Abstract

The attackers use social bots to steal people’s privacy

propagate fraud messages and influent public opinions

which has brought a great threat for personal privacy security

social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly

development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides

main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection

the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method

approaches from the previous research based on features

machine learning

graph and crowd sourcing were summarized.Also

the limitation of these methods in detection accuracy

computation cost and so on was dissected.At last

a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.

关键词

Keywords

references

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