ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2018, Vol. 55 ›› Issue (6): 1320-1332.doi: 10.7544/issn1000-1239.2018.20170231

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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

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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