A Malicious Users Detecting Model Based on Feedback Correlations
The trust and reputation models were introduced to restrain the impacts caused by rational but selfish peers in P2P streaming systems. However, these models face with two major challenges from dishonest feedback and strategic altering behaviors. To answer these challenges, the authors present a global trust model based on network community, evaluation correlations, and punishment mechanism. They also propose a two-layered overlay to provide the function of peers' behaviors collection and malicious detection. Furthermore, they analysis several security threats in P2P streaming systems, and discuss how to defend with them by their trust mechanism. The simulation results show that their trust framework can successfully filter out dishonest feedbacks by using correlation coefficients. It can effectively defend against the security threats with good load balance as well.