A Consecutive-Behaviors-Observing-Based Neighbor Evaluation Model in P2P Network

Reputation based trust mechanism has been identified as an effective method to evaluate peers behavior and which is employed to secure the applications in P2P network. Trust mechanism is such a mechanism that relies on other peers reports, which are also called local trust, to evaluate a designated peer. However, the existence of strategic peers and human judgment error is a big challenge, which makes the local trust hard to reflect peers type. Furthermore, it increases the estimation error of global trust. The authors propose a new model, called PeerStrategy, to evaluate neighbors behavior in P2P network. This model explores deterministic finite automaton (DFA) to describe the variance of neighbors consecutive behaviors. The DFA consists of seven states and it transits between states by neighbors performance in the interactions. By examining the probability of negative behaviors in any consecutive ones, the model can not only detect strategic peers accurately but also tolerate human judgment error. As a result, this model improves the accuracy of local trust, and whats more, it decreases the estimation error of global trust. The simulation shows that this model improves the accuracy of local trust considerately and also diminishes the influence on the estimation error of global trust, and it performs the best compared with other current methods.