A Malicious Users Detecting Model Based on Feedback Correlations

The trust and reputation models were introduced to restrain the impacts caused by rational but selfishpeers in P2P streaming systems. However, these models face with two major challenges from dishonestfeedback and strategic altering behaviors. To answer these challenges, we present a global trust modelbased on network community, evaluation correlations, and punishment mechanism. We also propose atwo-layered overlay to provide the function of peers’ behaviors collection and malicious detection.Furthermore, we analysis several security threats in P2P streaming systems, and discuss how to defendwith them by our trust mechanism. The simulation results show that our trust framework can successfullyfilter out dishonest feedbacks by using correlation coefficients. It can effectively defend against thesecurity threats with good load balance as well.