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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Title of Paper:A Collaborative Collusion Detection Method Based on Online Clustering

Journal:Jisuanji Yanjiu yu Fazhan

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. © 2018, Science Press. All right reserved.

ISSN No.:1000-1239

Translation or Not:no

Date of Publication:2018-06-01

Co-author:syr,Jin, Ting,Zhou, Liangguang

Correspondence Author:twa