A novel feedback based fast adaptive trust model for P2P networks

Peer-to-peer (P2P) networks have shown great potentials in providing a wide range of services starting from simple file sharing to distributed computing. However, P2P systems present ominous threats due to its anonymous and dynamic nature. One feasible way to minimize the threats is to evaluate the trust and reputation of the interacting peers. Trust models have often been deployed in determining the trust of peers in the network with the view to avoiding the malicious ones. Most of the existing trust models can successfully isolate malicious peers when the peers behave in a predictable way while others even fail to do so. On the other hand, these models suffer greatly when peers start to behave in a unpredictable way. Moreover, these models are ineffective in providing quick response to a peer's dynamic personality. To cope with such strategically altering behavior we present in this paper, a feedback based fast adaptive trust model which takes into account various factors in computing the trust of peers including recent trend, historical trend, sudden deviation of trust and so on. Simulations show that our model compared to other existing models can effectively identify and isolate the dynamic behavioral change of malicious peers.