基于交互行为的在线社会网络水军检测方法

doi:10.11959/j.issn.1000-436x.2015156

摘要/Abstract

摘要：

网络水军对广告、谣言、木马和恶意链接进行传播，不仅干扰用户对在线社会网络的正常访问，还可能引发网络安全、社会稳定等方面的问题。针对网络水军信息传播的特点，提出基于交互行为的信息传播模型。模型根据不同传播主体间的交互定义特征来量化传播行为，使用决策树方法对水军传播的信息进行检测。通过新浪微博的真实数据分析传播模型并验证检测方法，结果表明检测方法能够对微博中水军信息进行有效检测。

关键词: 网络水军, 检测, 交互行为, 信息传播, 在线社会网络

Abstract:

In online social networks,advertisements,rumors and malicious links are propagated by spammers arbitrarily.They not only disturb users’usualaccess,but also bring about network security threats and social panics.In an attempt to deal with the spam problems,an information diffusion model was proposed to capture the features of spam propagation.Propagation behaviors are quantitatively analyzed to detect spam messages with a decision tree-based method.The effectiveness of proposed detection model is evaluated with real data from the micro-bloggingnetwork of Sina.The experimental results show that proposed model can effectively detect spams in Sina micro-bloggingnetwork.

Key words: spam, detection, interaction, information diffusion, online social network

陈侃,陈亮,朱培栋,熊岳山. 基于交互行为的在线社会网络水军检测方法[J]. 通信学报, 2015, 36(7): 120-128.

Kan CHEN,Liang CHEN,Pei-dong ZHU,Yue-shan XIONG. Interaction based on method for spam detection in online social networks[J]. Journal on Communications, 2015, 36(7): 120-128.

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参考文献 18

[1]	[EB/OL].
[2]	[EB/OL].
[3]	[EB/OL].
[4]	RAYMOND Y K , STEPHEN L , LIAO S Y . Text mining and probabilistic language modeling for online review spam detection[J]. ACM Trans Management Inf Syst, 2011,2(4): 25.
[5]	GRIER C , THOMAS K , PAXSON V , et al. @spam:the underground on 140 characters or less[A]. Proceedings of the 17th ACM Conference on Computer and Communications Security[C]. Chicago,Illinois,USA, 2010. 27-37.
[6]	IRANI D , WEBB S , PU C . Study of static classification of social spam profiles in MySpace[A]. ICWSM[C]. 2010.
[7]	THOMAS K , GRIER C , SONG D , et al. Suspended accounts in retrospect:an analysis of twitter spam[A]. Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference[C]. Berlin,Germany, 2011. 243-258.
[8]	SHIN Y , GUPTA M , MYERS S . Prevalence and mitigation of forum spamming[A]. IEEE INFOCOM 2011[C]. 2011. 2309-2317.
[9]	BENEVENUTO F , RODRIGUES T , ALMEIDA V , et al. Identifying video spammers in online social networks[A]. Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web[C]. Beijing,China, 2008. 45-52.
[10]	RAJADESINGAN A . MAHENDRAN A . Comment spam classi fication in blogs through comment analysis and comment-blog post relationships[A]. Proceedings of the 13th International Conference on Computational Linguistics and Intelligent Text Processing-Volume Part II[C]. New Delhi,India: Springer-Verlag, 2012. 490-501.
[11]	HEYMANN P , KOUTRIKA G , GARCIA-MOLINA H . Fighting spam on social Web sites:a survey of approaches and future challenges[J]. IEEE Internet Computing, 2007,11(6): 36-45.
[12]	BENEVENUTO F , MAGNO G , RODRIGUES T , et al. Detecting spammers on twitter[A]. CEAS[C]. 2010.
[13]	WANG A H . Detecting spam bots in online social networking sites:a machine learning approach[A]. Data and Applications Security and Privacy,25th Anunual IFIP WG11.3 Conference[C]. 2010. 335-342.
[14]	苏金树, 张博锋, 徐昕等. 基于机器学习的文本分类技术研究进展[J]. 软件学报, 2006,19(9): 1848-1859. SU J S , ZHANG B F , XU X , et al. Advances in machine learning based text categorization[J]. Journal of Software, 2006,19(9): 1848-1859.
[15]	ZHANG X , ZHU S , LIANG W . Detecting spam and promoting campaigns in the Twitter social network[A]. The 12th IEEE International Conference on Data Mining[C]. 2012. 1194-1199.
[16]	GAO H , HU J , WILSON C , et al. Detecting and characterizing social spam campaigns[A]. The 10th ACM SIGCOMM Conference on Internet Measurement[C]. Melbourne,Australia, 2010.
[17]	CHEN C , WU K , SRINIVASAN V , et al. Battling the internet water army:detection of hidden paid posters[EB/OL]. arXiv preprint arXiv:1111.4297v1[cs.SI]. 2011.
[18]	韩忠明等. 面向微博的概率图水军识别模型[J]. 计算机研究与发展, 2013,S2: 180-186. HAN Z M , XU F M , DUAN D G . Probabilistic graphical model for identifying water army in microblogging system[J]. Journal of Computer Research and Development, 2013,S2: 180-186.

分类器	准确率	召回率	综合评价
决策树	0.999	0.956	0.977
SVM	0.985	0.670	0.798
RBF	0.969,	0.797	0.875

检测方法	准确率	召回率	综合评价
本文方法	0.999	0.956	0.977
文献[18]方法	0.90	0.72	0.80