KFTrust: P2P trust model based on evaluation rank using Kalman filter

Security and privacy have been critically important with the fast expansion of P2P systems, which are open, anonymous and dynamic in nature. Due to such nature of P2P systems, P2P networks present potential threats among nodes. One feasible way to minimise threats is to evaluate the trust values of the interacting nodes. Many trust models have done so, but they fail to properly evaluate the trust values when malicious nodes start to behave in an unpredictable way. To solve the strategically altering behaviour of malicious nodes, this paper designs an evaluation rank-based trust model according to the different recommended trust values. This model computes the trust value by combining the similarity between the nodes and the time decaying features. In the mechanism of finding recommended nodes, a trust-based Kalman filter algorithm is implemented to reduce the system overhead. Finally, in our experiments, we compare our model with other four typical models, the results show that our model is able to defend against malicious nodes' attack effectively and reduce the system overhead.