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

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.

A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

“…Reputation management has been studied in many computer science domains including e-commerce [12], multi-agent [13], peer-to-peer [14], and social network [15] systems. Reputation management can help entities choose a trustworthy and suitable partner so that risk can be minimized in future defective transactions [4].…”

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