Evolving a trust model for peer-to-peer networks using genetic programming

Abstract

Peer-to-peer (P2P) systems have attracted significant interest in recent years. In P2P networks, each peer act as both a server or a client. This characteristic makes peers vulnerable to a wide variety of attacks. Having robust trust management is very critical for such open environments to exclude unreliable peers from the system. This paper investigates the use of genetic programming to asses the trustworthiness of peers without a central authority. A trust management model is proposed in which each peer ranks other peers according to local trust values calculated automatically based on the past interactions and recommendations. The experimental results have shown that the model could successfully identify malicious peers without using a central authority or global trust values and, improve the system performance.