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Mobile Information Systems
Volume 2016, Article ID 5185170, 16 pages
http://dx.doi.org/10.1155/2016/5185170
Research Article

A Fuzzy Collusive Attack Detection Mechanism for Reputation Aggregation in Mobile Social Networks: A Trust Relationship Based Perspective

1College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
2The Third Research Institute of Ministry of Public Security, Shanghai 201204, China
3Anhui Boryou Information Technology Co., Ltd., Hefei, Anhui 230000, China

Received 21 November 2015; Revised 25 February 2016; Accepted 6 March 2016

Academic Editor: Claudio Agostino Ardagna

Copyright © 2016 Bo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

While the mechanism of reputation aggregation proves to be an effective scheme for indicating an individual’s trustworthiness and further identifying malicious ones in mobile social networks, it is vulnerable to collusive attacks from malicious nodes of collaborative frauds. To conquer the challenge of detecting collusive attacks and then identifying colluders for the reputation system in mobile social networks, a fuzzy collusive attack detection mechanism (FCADM) is proposed based on nodes’ social relationships, which comprises three parts: trust schedule, malicious node selection, and detection traversing strategy. In the first part, the trust schedule provides the calculation method of interval valued fuzzy social relationships and reputation aggregation for nodes in mobile social networks; further, a set of fuzzy valued factors, that is, item judgment factor, node malicious factor, and node similar factor, is given for evaluating the probability of collusive fraud happening and identifying single malicious nodes in the second part; and moreover, a detection traversing strategy is given based on random walk algorithm under the perspectives of fuzzy valued nodes’ trust schedules and proposed malicious factors. Finally, our empirical results and analysis show that the proposed mechanism in this paper is feasible and effective.