ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (6): 1320-1332.doi: 10.7544/issn1000-1239.2018.20170231

• 软件技术 • 上一篇    下一篇

基于在线聚类的协同作弊团体识别方法

孙勇1,2,谭文安1,金婷1,周亮广2   

  1. 1(南京航空航天大学计算机科学与技术学院 南京 211106); 2(安徽省地理信息集成应用协同创新中心(滁州学院) 安徽滁州 239000) (ysun.nuaa@yahoo.com)
  • 出版日期: 2018-06-01
  • 基金资助: 
    国家自然科学基金项目(61672022,61272036);安徽省高校自然科学基金重点项目(KJ2017A414)

A Collaborative Collusion Detection Method Based on Online Clustering

Sun Yong1,2, Tan Wenan1, Jin Ting1, Zhou Liangguang2   

  1. 1(College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106); 2(Anhui Center for Collaborative Innovation in Geographical Information Integration and Application (Chuzhou University), Chuzhou, Anhui 239000)
  • Online: 2018-06-01

摘要: 针对大规模服务计算环境中聚集反馈、协同作弊和虚假评价等问题,通过融合在线聚类与共谋欺骗检测技术,提出了一种支持大规模服务可信度分析的在线协同作弊用户发现方法.首先,根据大规模服务系统日志中用户反馈评分信息,综合考虑大规模服务计算的大数据特性问题,设计了一种新颖的基于改进更新规则的在线KMeans聚类算法:在基于随机梯度法的在线聚类算法的基础上,采用了一种改进的基于小批量学习的在线聚类方法;并且,通过自动修正权重的聚类分组方差计算,进行递减增量优化,提高了在线KMeans算法的聚类质量,同时保证了聚类算法的时间效率;然后,充分考虑了协同作弊团体的同谋行为特征和协同攻击现象,利用聚类分组的性质和同谋团体异常性的特征,检测出协同作弊团体.仿真实验结果表明:提出的基于在线聚类的协同作弊团体识别方法具有良好时间性能,有效地解决了大规模服务计算中虚假反馈的问题.

关键词: 协同计算, 协同作弊识别, 在线聚类, 可信服务计算, 面向服务的云应用

Abstract: Cloud computing has been successfully used to integrate various Web services for facilitating the automation of large-scale distributed applications. However, there exist numerous noise ratings given in service-oriented cloud applications by collusion groups. Collusion detection is one of the most import issues in the emerging service-oriented cloud applications. Especially with the emergence of massive Web services, it is still a tough challenge to identify collaborative collusion groups in large-scale cloud systems using the classical clustering algorithm with batch computing mode. To tackle the challenge, a novel online clustering-based detection method is proposed to find collaborative collusion groups in an efficient and effective manner. Firstly, a mini-batch KMeans clustering method is employed to reduce the computational time for mining the large-scale service data; secondly, to improve the quality of the online clustering, a new and modified update rule is designed for the mini-batch KMeans clustering method, which adaptively optimizes the clustering weights with variance through an iterative procedure; finally, based on measuring the behavior similarity and group ratings deviation of malicious peers, a binary decision diagram evaluation method is presented for detecting the bias and prestige of collusion groups in a visual manner. Theoretical analysis is conducted for validation purpose. Extensive experimentation and comparison with related work indicate that the proposed approach is feasible and effective.

Key words: collaborative computing, collaborative collusion detection, online clustering, trust service computing, service-oriented cloud applications

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