基于图卷积网络的社交网络Spammer检测技术

doi:10.11959/j.issn.2096-109x.2018042

Abstract

Abstract:

In social networks,Spammer send advertisements that are useless to recipients without the recipient's permission,seriously threatening the information security of normal users and the credit system of social networking sites.In order to solve problems of extracting the shallow features and high computational complexity for the existing Spammer detection methods of social networks,a Spammer detection technology based on graph convolutional network(GCN) was proposed.Based on the network structure information,the method introduces the network representation learning algorithm to extract the network local structure feature,and combines the GCN algorithm under the re-regularization technology condition to obtain the network global structure feature to achieve the goal of detecting Spammer.Experiments are done on social network data of Tagged.com.The results show that this method has high accuracy and efficiency.

Key words: cyberspace security, Spammer detection, network representation learning, GCN

CLC Number:

TP309

Qiang QU, Hongtao YU, Ruiyang HUANG. Spammer detection technology of social network based on graph convolution network[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 39-46.

Figures/Tables 8

References 25

[1]	MA HUATENG , . Normal and reasonable competition is good for business[C]// World Fortune Forum. 2017.
[2]	HAROLD NGUYEN . 2013 state of social media spam.Technical report[R]. 2013
[3]	STRINGHINI G , KRUEGEL C , VIGNA G . Detecting Spammers on social networks[C]// The 26th Annual Computer Security Applications Conference. 2010: 1-9.
[4]	RAYANA S , AKOGLU L . Collective opinion spam detection:bridging review networks and metadata[C]// The 21th ACM Sigkdd International Conference On Knowledge Discovery And Data Mining. 2015: 985-994.
[5]	LIM E P , NGUYEN V A , JINDAL N ,et al. Detecting product review Spammers using rating behaviors[C]// The 19th ACM International Conference On Information and Knowledge Management. 2010: 939-948.
[6]	BENEVENUTO F , MAGNO G , RODRIGUES T ,et al. Detecting Spammers on twitter[C]// Collaboration,Electronic Messaging,Anti-Abuse And Spam Conference (CEAS 2010). 2010:12.
[7]	GAO H , HU J , WILSON C ,et al. Detecting and characterizing social spam campaigns[C]// The 10th ACM SIGCOMM Conference on Internet Measurement. 2010: 35-47.
[8]	HU X , TANG J , ZHANG Y ,et al. Social Spammer detection in microblogging[C]// (IJCAI 2013). 2013: 2633-2639.
[9]	HU X , TANG J , LIU H . Online social Spammer detection[C]// AAAI. 2014: 59-65.
[10]	HU X , TANG J , GAO H ,et al. Social Spammer detection with sentiment information[C]// 2014 IEEE International Conference on Data Mining (ICDM). 2014: 180-189.
[11]	KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks[C]// ICLR. 2017.
[12]	涂存超, 杨成, 刘知远 ,等. 网络表示学习综述[J]. 科学通报, 1998,43: 1681.
	TU C C , YANG C , LIU Z Y ,et al. Network representation learning:an overview[J]. Chinese Science Bulletin, 1998,43: 1681.
[13]	CUI P , WANG X , PEI J ,et al. A survey on network embedding[J]. Social and Information Networks(Cornell University Library), 2017.
[14]	PEROZZI B , AL-RFOU R , SKIENA S . DeepWalk:online learning of social representations[C]// The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014: 701-710.
[15]	GROVER A , LESKOVEC J . Node2vec:scalable feature learning for networks[C]// The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 855-864.
[16]	RIBEIRO L F R , SAVERESE P H P , FIGUEIREDO D R . Struc2vec:learning node representations from structural identity[C]// The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 385-394.
[17]	TANG J , QU M , WANG M ,et al. Line:large-scale information network embedding[C]// The 24th International Conference on World Wide Web. 2015: 1067-1077.
[18]	HENAFF M , BRUNA J , LECUN Y . Deep convolutional networks on graph-structured data[J]. Computer Science, 2015.
[19]	HE K , ZHANG X , REN S ,et al. Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
[20]	JAIN A , ZAMIR A R , SAVARESE S ,et al. Structural-RNN:deep learning on spatio-temporal graphs[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5308-5317.
[21]	LI Y , TARLOW D , BROCKSCHMIDT M ,et al. Gated graph sequence neural networks[J]. Computer Science, 2015.
[22]	SHUMAN D I , NARANG S K , FROSSARD P ,et al. The emerging field of signal processing on graphs:extending high-dimensional data analysis to networks and other irregular domains[J]. IEEE Signal Processing Magazine, 2013,30(3): 83-98.
[23]	HAMMOND D K , VANDERGHEYNST P , GRIBONVAL R . Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011,30(2): 129-150.
[24]	DEFFERRARD M , BRESSON X , VANDERGHEYNST P . Convolutional neural networks on graphs with fast localized spectral filtering[C]// Advances in Neural Information Processing Systems. 2016: 3844-3852.
[25]	CHUNG F R K , . Spectral graph theory[C]// CBMS Regional Conference Series in Mathematics. 1996.

Metrics

Recommended 0

No Suggested Reading articles found!

异常用户种类	主要存在平台
Spammer（垃圾信息发送者）	Twitter，微博平台，CNN，亚马孙， Yelp
Hoaxes（文化攻击者）	维基百科
Bot（机器人）	Twitter
Sockpuppet（马甲）	维基百科，Twitter，Facebook，CNN
Troll（钓鱼者）	Slashdot zoo，维基百科
Fake user（虚假用户）	Google，Twitter，Amazon
Vandal（恶意用户）	Twitter，维基百科，TCP

名称	迭代形式	权函数W(x)	区间
Chebyshev-I			?1,1]
Chebyshev-II			?1,1]
Legendre		1	?1,1]
Languerre		e^{? x}	0,+∞)
Hermite			(?∞,+∞)

用户数据集字段	字段含义	关系数据集字段	字段含义
userID	匿名化ID	day	匿名天数：[0,1,…,9]
sex	性别：M/F	time_ms	每天的毫秒数
timePassedValidation	时间流逝验证：0—1	src	发送者的ID
ageGroup	年龄段：[10,20,30,…]	dst	接收者的ID
label	标签：1(Spammer)/0(normal user)	relation	关系的匿名化ID：[0,1,…,6]

算法		准确率
算法	DeepWalk	Node2vec	Struc2vec
基准线	52.82%	53.73%	82.26%
感知机	52.93%	55.00%	84.07%
逻辑回归	60.87%	58.20%	86.07%
随机森林	54.13%	58.13%	85.60%
GCN	70.47%	65.80%	81.67%

Spammer detection technology of social network based on graph convolution network

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 25

Related Articles 12

Metrics

Recommended 0

[1]	Guyue LI, Aiqun HU. Exploration and practice on integration of ideological, political courses into professional courses of cyberspace security specialty [J]. Chinese Journal of Network and Information Security, 2022, 8(2): 183-189.
[2]	Fu XIAO, Haiping HUANG, Sujun HU, Lijuan SUN. Research on the cultivation of cyber security talents under the perspective of double tops based on Jiangsu province [J]. Chinese Journal of Network and Information Security, 2021, 7(2): 1-9.
[3]	ZHANG Huanguo,DU Ruiying. Introduction to cyberspace security discipline [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 4-18.
[4]	XU Tongge,LIU Lianzhong,LIU Jianwei,MAO Jian. Preliminary study on the cultivation mode of innovative talents in the first-class cyber security college [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 19-24.
[5]	HU Aiqun,LI Guyue,PENG Linning,LI Tao. Exploration of teaching method incorporating ideological elements for the cutting-edge technology of cyberspace security [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 54-66.
[6]	CHEN Kai,FU Cai,ZOU Deqing,QIANG Weizhong,CUI Yongquan,HAN Lansheng. Construction of cyberspace security course system of comprehensive practice and graded customs clearance [J]. Chinese Journal of Network and Information Security, 2019, 5(3): 67-74.
[7]	Ying YIN,Lixin JI,Ruiyang HUANG,Lixin DU. Research and development of network representation learning [J]. Chinese Journal of Network and Information Security, 2019, 5(2): 77-87.
[8]	Quan REN,Lei HE,Jiangxing WU. Analysis of different anti-interference system models based on discrete time Markov chain [J]. Chinese Journal of Network and Information Security, 2018, 4(4): 30-37.
[9]	De-yu YUAN,Xiao-juan WANG,Jian-chao WAN. Influence of Internet plus on cyberspace security and the technology development trend in Internet plus era [J]. Chinese Journal of Network and Information Security, 2017, 3(5): 1-9.
[10]	Bin-xing FANG. A hierarchy model on the research fields of cyberspace security technology [J]. Chinese Journal of Network and Information Security, 2015, 1(1): 2-7.
[11]	Feng-hua LI. Development trends of the information technology and cyberspace security [J]. Chinese Journal of Network and Information Security, 2015, 1(1): 8-17.
[12]	Jian-ming ZHU,Qin WANG. Analysis of cyberspace security based on game theory [J]. Chinese Journal of Network and Information Security, 2015, 1(1): 43-49.