Multistage Filtering for Collusion Detection in P2P Network

Peer-to-Peer (P2P) reputation management system based on social network often relies on the nodes´ feedbacks and is needed to evaluate the trustworthiness of participating peers to combat malicious peer behaviors. Some peers collude and organize a collusive group in P2P file sharing system. The collusive group is harmful and easy to be confused with union that is composed by peers with same partial. We proposed a novel approach to evaluate a suspected group as a whole to distinguish clique from union. Besides this, in each estimation process, peers download a subset of trust ratings of feedbacks instead of all and make up difference caused by feedbacks reducing through repeated transaction accumulation. Simulation experiments demonstrate the system is accurate and decreases downloading data of estimation trust process. The results show the approach is proper for networks with moderate ratio of malicious peers.