基于传播时空特性的社交网络灰帽用户检测

doi:10.19678/j.issn.1000-3428.0059636

计算机工程 ›› 2021, Vol. 47 ›› Issue (12): 192-199. doi: 10.19678/j.issn.1000-3428.0059636

基于传播时空特性的社交网络灰帽用户检测

何欢¹, 朱焱¹, 李春平²

1. 西南交通大学信息科学及技术学院, 成都 611756;
2. 清华大学软件学院, 北京 100091

收稿日期:2020-10-01 修回日期:2020-12-02 发布日期:2020-12-09
作者简介:何欢(1996-),女,硕士研究生,主研方向为社交网络、数据挖掘;朱焱(通信作者),教授、博士、博士生导师;李春平,副教授、博士。
基金资助:
四川省科技计划项目（2019YFSY0032）。

Grey Hat User Detection in Social Network Based on Spatiotemporal Characteristics of Diffusion

HE Huan¹, ZHU Yan¹, LI Chunping²

1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China;
2. School of Software, Tsinghua University, Beijing 100091, China

Received:2020-10-01 Revised:2020-12-02 Published:2020-12-09

摘要/Abstract

摘要： 社交网络灰帽用户极易隐藏且类型多样，导致现有检测算法适用性较差。提出一种基于传播时空特性的社交网络检测算法。构建用户生成内容传播网络度量白帽和灰帽用户在传播空间上的不同特性，融合时空传播特性并调节权重比例以提高分类性能。实验结果表明，该算法能有效检测不同类型灰帽用户，与用户特征分析、社交网络链接分析、多视图融合等主流灰帽用户检测算法相比，其在CAVERLEE、CRESCI-15、CRESCI-17等多个数据集上的准确率及AUC值最高分别提升26.08%和30.54%。

关键词: 社交网络, 灰帽用户, 网络传播, 特征融合, 用户检测

Abstract: Grey hat users in social networks are diverse and good at disguising, which reduces the generability of detection algorithms.To address the problem, this paper proposes a social network detection mechanism based on the spatiotemporal characteristics of diffusion, Diffusion Spatio-Temporal Characteristics(DSTC).By using the characteristics of the diffusion time sequence of the content generated by white hat/grey hat users, a User Generated Content(UGC) diffusion network is constructed to measure the different characteristics of the white/grey hat users in the diffusion space.Based on the spatio-temporal characteristics of diffusion, a mechanism is designed for social network user detection, which integrates the spatio-temporal characteristics of diffusion and adjusts the weight ratio to improve classification performance.Experimental results show that compared with the mainstream grey hat user detection algorithms, which are based on user characteristic analysis, social network link analysis or multi-view fusion, the proposed algorithm exhibits a significant improvement in accuracy and AUC value, which is increased by up to 26.08% and 30.54% respectively on data sets like CAVERLEE, CRESCI-15, CRESCI-17, et al, indicating that DSTC can effectively detect different types of potential grey hat users.

Key words: social network, grey hat user, network diffusion, feature fusion, user detection

中图分类号:

TP393.09

何欢, 朱焱, 李春平. 基于传播时空特性的社交网络灰帽用户检测[J]. 计算机工程, 2021, 47(12): 192-199.

HE Huan, ZHU Yan, LI Chunping. Grey Hat User Detection in Social Network Based on Spatiotemporal Characteristics of Diffusion[J]. Computer Engineering, 2021, 47(12): 192-199.

https://www.ecice06.com/CN/Y2021/V47/I12/192

图/表 6

20211214181629

20211214181632

20211214181635

20211214181638

20211214181642

20211214181645

参考文献

[1] 吴越, 陈晓亮, 蒋忠远.微博信息流行度预测研究综述[J].西华大学学报(自然科学版), 2017(1):1-6. WU Y, CHEN X L, JIANG Z Y.Survey on predicting popularity of information in microblogs[J].Journal of Xihua University(Natural Science Edition), 2017(1):1-6.(in Chinese)
[2] ERÇAHIN B, AKTAŞ Ö, KILINÇ D, et al.Twitter fake account detection[C]//Proceedings of the 2nd International Conference on Computer Science and Engineering.Washington D.C., USA:IEEE Press, 2017:388-392.
[3] RAYMOND Y K, STEPHEN L, LIAO S Y.Text mining and probabilistic language modeling for online review spam detection[J].ACM Transactions on Management Information Systems, 2011, 2(4):25-30.
[4] ZHANG X, ZHU S, LIANG W.Detecting spam and promoting campaigns in the twitter social network[C]//Proceedings of the 12th IEEE International Conference on Data Mining.Washington D.C., USA:IEEE Press, 2012:1194-1199.
[5] NEIL Z G, MARIO F, PRATEEK M.Sybil belief:a semi-supervised learning approach for structure-based sybil detection[J].IEEE Transactions on Information Forensics and Security, 2014, 9(6):976-987.
[6] JIA J Y, WANG B H, GONG N Z Q.Random walk based fake account detection in online social networks[C]//Proceedings of the 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks.Washington D.C., USA:IEEE Press, 2017:273-284.
[7] WANG B, ZHANG L, GONG N Z.Sybilscar:Sybil detection in online social networks via local rule based propagation[C]//Proceedings of 2017 IEEE Conference on Computer Communications.Washington D.C., USA:IEEE Press, 2017:1-9.
[8] YANG Z, WILSON C, WANG X, et al.Uncovering social network sybils in the wild[J].ACM Transactions on Knowledge Discovery from Data, 2014, 8(1):1-29.
[9] SRIDHARAN V, SHANKAR V, GUPTA M.Twitter games:How successful spammers pick targets[C]//Proceedings of the 28th Annual Computer Security Applications Conference.New York, USA:ACM Press, 2012:389-398.
[10] BOSHMAF Y, MUSLUKHOV I, BEZNOSOV K, et al.The social bot network:when bots socialize for fame and money[C]//Proceedings of the 27th Annual Computer Security Applications Conference.New York, USA:ACM Press, 2011:93-102.
[11] KOLL D, SCHWARZMAIER M, LI J, et al.Thank you for being a friend:an attacker view on online-social-network-based sybil defenses[C]//Proceedings of the 37th IEEE International Conference on Distributed Computing Systems Workshops.Washington D.C., USA:IEEE Press, 2017:157-162.
[12] EFFENDY S, YAP R H.The strong link graph for enhancing sybil defenses[C]//Proceedings of the 37th IEEE International Conference on Distributed Computing Systems.Washington D.C., USA:IEEE Press, 2017:944-954.
[13] ZHANG X, XIE H, LUI J C.Sybil detection in social-activity networks:modeling, algorithms and evaluations[C]//Proceedings of the 26th IEEE International Conference on Network Protocols.Washington D.C., USA:IEEE Press, 2018:44-54.
[14] STEFANO C, ROBERTO D, MARINELLA P, et al.Social fingerprinting:detection of spambot groups through DNA-inspired behavioral modeling[J].IEEE Transactions on Dependable and Secure Computing, 2018, 15(4):561-576.
[15] MATEEN M, IQBAL M A, ALEEM M, et al.A hybrid approach for spam detection for Twitter[C]//Proceedings of the 14th International Bhurban Conference on Applied Sciences and Technology.Washington D.C., USA:IEEE Press, 2017:466-471.
[16] CHAOZHUO L, SENZHANG W, LIFANG H, et.al.SSDMV:semi-supervised deep social spammer detection by multi-view data fusion[C]//Proceedings of the 18th IEEE International Conference on Data Mining.Washington D.C., USA:IEEE Press, 2018:247-256.
[17] LIU Y, WU B, WANG B, et al.SDHM:a hybrid model for spammer detection in Weibo[C]//Proceedings of 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.New York, USA:ACM Press, 2014:942-947.
[18] GROVER A, LESKOVEC J.Node2vec:scalable feature learning for networks[C]//Proceedings of 2016 ACM Sigkdd International Conference on Knowledge Discovery & Data Mining.New York, USA:ACM Press, 2016:855-864.
[19] KYUMIN L, EOFF B D, CAVERLEE J.Seven months with the devils:a long-term study of content polluters on Twitter[EB/OL].[2020-09-01].https://www.researchgate.net/publication/221297999_Seven_Months_with_the_Devils_A_Long-Term_Study_of_Content_Polluters_on_Twitter.
[20] YANG K C, VAROL O, HUI P M, et.al.Scalable and generalizable social bot detection through data selection[EB/OL].[2020-09-01].https://arxiv.org/abs/1911.09179.
[21] CRESCI S, PIETRO D R, PETROCCHI M, et.al.Fame for sale:efficient detection of fake Twitter followers[J].Decision Support Systems, 2015(80):56-71.

选择文件类型/文献管理软件名称

选择包含的内容

基于传播时空特性的社交网络灰帽用户检测

Grey Hat User Detection in Social Network Based on Spatiotemporal Characteristics of Diffusion

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李俊仪, 李向阳, 龙朝勋, 李海燕, 李红松, 余鹏飞. 基于多级区域选择与跨层特征融合的野生菌分类[J]. 计算机工程, 2024, 50(9): 179-188.
[2]	张华青, 夏张涛, 陆晓庆, 童基均. 基于字形特征的血管外科命名实体识别[J]. 计算机工程, 2024, 50(8): 13-21.
[3]	李华昱, 张智康, 闫阳, 岳阳. 基于知识图谱增强的领域多模态实体识别[J]. 计算机工程, 2024, 50(8): 31-39.
[4]	刘锁兰, 王炎, 王洪元, 朱生升. 基于多流语义图卷积网络的人体行为识别[J]. 计算机工程, 2024, 50(8): 64-74.
[5]	赵婉秋, 张俊虎, 李海涛. 用于建筑物分割的平行结构特征融合网络[J]. 计算机工程, 2024, 50(8): 239-248.
[6]	赵宏, 王枭. 基于Swin-Transformer的黑色素瘤图像病灶分割研究[J]. 计算机工程, 2024, 50(8): 249-258.
[7]	王富平, 刘鸿玮, 张锲石, 段冠庄. 基于深度特征抑制的遮挡人脸识别网络[J]. 计算机工程, 2024, 50(8): 259-269.
[8]	闵莉, 董冰洁, 安冬. 基于多注意力机制与跨特征融合的语义分割算法[J]. 计算机工程, 2024, 50(8): 282-289.
[9]	陈宇航, 杨勇, 先木斯亚·买买提明, 帕力旦·吐尔逊, 樊小超, 任鸽, 刁宇峰. 基于主题感知和语义增强的作文自动评分方法[J]. 计算机工程, 2024, 50(8): 363-371.
[10]	钱榕, 李鑫, 刘晓豫, 张克君, 郭竞桧, 叶仲昆. 考虑禁言机制的谣言传播模型[J]. 计算机工程, 2024, 50(8): 372-378.
[11]	谭巨全, 王然. 特征融合下田径录像3D人体动作DTW捕捉算法[J]. 计算机工程, 2024, 50(7): 71-78.
[12]	张溢文, 蔡满春, 陈咏豪, 朱懿, 姚利峰. 融合空间特征的多尺度深度伪造检测方法[J]. 计算机工程, 2024, 50(7): 240-250.
[13]	王晋涛, 秦昂, 张元, 陈一飞, 王廷凤, 谢承霖, 邹刚. 基于注意力增强与特征融合的中文医学实体识别[J]. 计算机工程, 2024, 50(7): 324-332.
[14]	李亚康, 陈刚. 小角中子散射物理模型自动化筛选[J]. 计算机工程, 2024, 50(6): 56-64.
[15]	杨硕, 王一丁. 基于改进薄板样条运动模型的人脸动画算法[J]. 计算机工程, 2024, 50(6): 255-265.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于传播时空特性的社交网络灰帽用户检测

Grey Hat User Detection in Social Network Based on Spatiotemporal Characteristics of Diffusion

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献

相关文章 15

编辑推荐

Metrics

本文评价