Adaptive approach to restraining content pollution in peer-to-peer networks

Abstract

Peer-to-Peer (P2P) networks face the challenge of frequent pollution attacks. In such attacks, malicious peers pollute the network by sharing mislabeled, corrupt or infected content in an attempt to disrupt the system and waste network resources. When faced by such phenomenon, regular peers get discouraged from participating in the P2P network as they find less value in the system. In this work, we investigate the amount of resources required to restrain pollution attacks by means of content validation. We introduce multiple adaptive techniques that can minimize the spread of polluted content, while at the same time reduce the cost of content validation for peers participating in the network. Furthermore, the proposed pollution-restraint techniques are resistant to collusion from malicious peers, and they do not contribute to excessive communication overhead in the P2P network.