Collusion Detection in Reputation Systems for Peer-to-Peer Networks

In peer-to-peer networks (P2Ps), many autonomous nodes without preexisting trust relationships share resources (e.g., files) between each other. Due to their open environment, P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high system reliability and security. A reputation system computes and publishes reputation score for each node based on a collection of opinions from others about the node. However, collusion behaviors impair the effectiveness of reputation systems in trustworthy node selection. Though many reputation calculation methods have been proposed to mitigate collusion's influence, little effort has been devoted to specifically tackling collusion. In this paper, we analyze transaction ratings in the Amazon and Overstock online transaction platforms during one year. The analysis of real trace confirms the existence of collusion as well as its important behavior characteristics and influence on reputation values in real reputation systems. Accordingly, we propose a collusion detection method to specifically thwart collusion behaviors. We further optimize the method by reducing the computing cost. Experimental results show that the proposed method can significantly enhance the capability of existing reputation systems to deter collusion with low cost.