Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
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CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
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A clustering approach to filtering unfair testimonies for reputation systems
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ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack, and it can significantly deteriorate the performances of the network. The existing solutions for coping with bad mouthing are mainly concentrated on prevention techniques. In this work we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrates the capability of the approach in detecting bad mouthing attack in various scenarios.