计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 251-255.doi: 10.11896/jsjkx.190600172
宋畅,禹可,吴晓非
SONG Chang,YU Ke,WU Xiao-fei
摘要: 社交媒体系统为人们提供了便利的共享、交流和协作平台。人们在享受社交媒体的开放性和便利性时,可能会发生许多恶意行为,例如欺凌、恐怖袭击计划和欺诈信息传播。因此,尽可能准确、及早地发现这些异常活动,以防止灾难和袭击,是非常重要的。近年来,随着在线社交网络(OSN)如Twitter,Facebook,Google+,LinkedIN等的成功,丰厚的利益资源使得它们成为了攻击者的目标。社交网络的开放性,使其特别容易受到异常账号攻击的威胁。现有基于图形的最先进分类模型大多使用首先为图的边分配权重,在加权图中迭代地传播节点的信誉分数,并使用最终的后验分数来对节点进行分类的方法。边权重的分配是其中一项重要的任务,此参数将直接影响检测结果的准确度。为此,文中针对社交媒体中异常账号的检测任务,分析了基于社交图全局结构的方法,通过在成对马尔可夫随机场模型中改进边权重的计算方法,使其能够在迭代过程中自适应优化,提出了准确度更高的GANG+LW,GANG+LOGW和GANG+PLOGW算法。这3种算法使用了不同的改进边权重的方法。实验证明,新提出的方法相对于基本的成对马尔可夫随机场模型,可取得更准确的异常账号检测结果,3种算法中GANG+PLOGW得到的结果最好。结果证明,此改进模型在检测社交网络中的异常账号时,能够更有效地解决问题。
中图分类号:
[1]YU R,QIU H D,WEN Z,et al.A Survey on Social Media Anomaly Detection[J].ACM SIGKDD Explorations Newsletter,2016,18(1):1-4. [2]GAO P,WANG B,GONG N Z,et al.Sybilfuse:Combining local attributes with global structure to perform robust sybil detection[J].arXiv:1803.06772,2018. [3]YANG Z,XUE J,YANG X,et al.VoteTrust:Leveraging friend invitation graph to defend against social network sybils[J].IEEE Transactions on Dependable and Secure Computing,2016,13(4):488-501. [4]MISRA S,TAYEEN A S M,XU W.SybilExposer:An effective scheme to detect Sybil communities in online social networks[C]∥2016 IEEE International Conference on Communications (ICC).IEEE,2016:1-6. [5]ZHENG H,XUE M,LU H,et al.Smoke screener or straight shooter:Detecting elite sybil attacks in user-review social networks[J].arXiv:1709.06916,2017. [6]DAVIS C A,VAROL O,FERRARA E,et al.BotOrNot:A system to evaluate social bots[C]∥Proceedings of the 25th International Conference Companion on World Wide Web.International World Wide Web Conferences Steering Committee,2016:273-274. [7]SAVAGE D,ZHANG X Z,YU X H,et al.Anomaly Detection in Online Social Network[J].Social Network,2014,39:62-70. [8]YANG Z,WILSON C,WANG X,et al.Uncovering social network sybils in the wild[J].ACM Transactions on Knowledge Discovery from Data (TKDD),2014,8(1):2. [9]GATTERBAUER W,GÜNNEMANN S,KOUTRA D,et al. Linearized and single-pass belief propagation[J].Proceedings of the VLDB Endowment,2015,8(5):581-592. [10]LIU Y,CHAWLA S.Social media anomaly detection:Challenges and solutions[C]∥Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.ACM,2017:817-818. [11]BIMAL V,BASHIR M A,MARK C,et al.Towards Detecting Anomalous User Behavior in Online Social Networks[M]∥Lecture Notes in Computer Science.Berlin:Springer,2014:223-238. [12]GONG N Z,FRANK M,MITTAL P.SybilBelief:A semi-supervised learning approach for structure-based sybil detection[J].IEEE Transactions on Information Forensics and Security,2014,9(6):976-987. [13]GAO P,GONG N Z,KULKARNI S,et al.Sybilframe:A de-fense-in-depth framework for structure-based sybil detection[J].arXiv:1503.02985,2015. [14]WANG B,ZHANG L,GONG N Z.SybilBlind:Detecting Fake Users in Online Social Networks without Manual Labels[J].arXiv:1806.04853,2018. [15]WANG B,JIA J,GONG N Z.Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation[J].arXiv:1812.01661,2018. [16]WANG B,GONG N Z,FU H.GANG:Detecting fraudulent users in online social networks via guilt-by-association on directed graphs[C]∥2017 IEEE International Conference on Data Mi-ning (ICDM).IEEE,2017:465-474. [17]WANG B,ZHANG L,GONG N Z.SybilSCAR:Sybil detection in online social networks via local rule based propagation[C]∥2017 IEEE International Conference on Computer Communications.IEEE,2017. [18]YANG C,HARKREADER R,ZHANG J L,et al.Analyzing Spammers’ Social Networks For Fun and Profit:A Case Study of Cyber Criminal Ecosystem on Twitter[C]∥Proceedings of the 21st International World Wide Web.New York:ACM,2012. |
[1] | 周旭, 钱胜胜, 李章明, 方全, 徐常胜. 基于对偶变分多模态注意力网络的不完备社会事件分类方法 Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification 计算机科学, 2022, 49(9): 132-138. https://doi.org/10.11896/jsjkx.220600022 |
[2] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[3] | 么晓明, 丁世昌, 赵涛, 黄宏, 罗家德, 傅晓明. 大数据驱动的社会经济地位分析研究综述 Big Data-driven Based Socioeconomic Status Analysis:A Survey 计算机科学, 2022, 49(4): 80-87. https://doi.org/10.11896/jsjkx.211100014 |
[4] | 戴宏亮, 钟国金, 游志铭, 戴宏明. 基于Spark的舆情情感大数据分析集成方法 Public Opinion Sentiment Big Data Analysis Ensemble Method Based on Spark 计算机科学, 2021, 48(9): 118-124. https://doi.org/10.11896/jsjkx.210400280 |
[5] | 张志扬, 张凤荔, 谭琪, 王瑞锦. 基于深度学习的信息级联预测方法综述 Review of Information Cascade Prediction Methods Based on Deep Learning 计算机科学, 2020, 47(7): 141-153. https://doi.org/10.11896/jsjkx.200300130 |
[6] | 张志扬, 张凤荔, 陈学勤, 王瑞锦. 基于分层注意力的信息级联预测模型 Information Cascade Prediction Model Based on Hierarchical Attention 计算机科学, 2020, 47(6): 201-209. https://doi.org/10.11896/jsjkx.200200117 |
[7] | 林敏鸿, 蒙祖强. 基于注意力神经网络的多模态情感分析 Multimodal Sentiment Analysis Based on Attention Neural Network 计算机科学, 2020, 47(11A): 508-514. https://doi.org/10.11896/jsjkx.191100041 |
[8] | 拥措, 史晓东, 尼玛扎西. 短文本情感分析的研究现状 ——从社交媒体到资源稀缺语言 Research Status of Sentiment Analysis for Short Text ——From Social Media to Scarce Resource Language 计算机科学, 2018, 45(6A): 46-49. |
[9] | 徐程浩,郭斌,欧阳逸,翟书颖,於志文. 基于社交媒体的事件感知与多模态事件脉络生成 Event Sensing and Multimodal Event Vein Generation Leveraging Social Media 计算机科学, 2017, 44(Z6): 33-36. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.007 |
[10] | 朱裴松,钱铁云,吴闽泉. 基于社会化表示的用户性别识别 Identifying Users’ Gender via Social Representations 计算机科学, 2017, 44(Z11): 160-165. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.033 |
[11] | 李敏,肖盛,刘正捷,张军. 嘀咕网用户领域影响力研究 Research on Field Influence of Digu Users 计算机科学, 2015, 42(9): 66-69. https://doi.org/10.11896/j.issn.1002-137X.2015.09.014 |
[12] | 李春彦,王良民. 车载自组网Sybil攻击检测方案研究综述 Research on Detection Schemes of Sybil Attack in VANETs 计算机科学, 2014, 41(Z11): 235-240. |
[13] | 王峰,李亚,朱海,王迤然. 一种基于蚁群算法的Sybil攻击防御 Sybil Attack Defense Based on Ant Colony Algorithm 计算机科学, 2013, 40(6): 100-102. |
[14] | 刘悦,李强,李舟军. P2P网络安全及防御技术研究综述 Survey of P2P Network Security and Defense Mechanism 计算机科学, 2013, 40(4): 9-13. |
[15] | 周君. 基于时空马尔可夫随机场交通参数采集系统研究 Research on Traffic Parameter Acquisition System Based on ST-MRF Model 计算机科学, 2013, 40(10): 292-295. |
|