Outlier detection in reputation management system for P2P networks using rough set theory

Peer-to-peer (P2P) networks are distributed, decentralized, dynamic networks that are self-organized and self-managed. P2P networks have emerged over the past several years as an effective and scalable medium for sharing distributed resources. However, determining the reliability and trustworthiness of the participating peers still remains a major security challenge. Reputation-based trust management calculates peer trust as a measure of recommendations received from other peers. Malicious peers may give wrong reputation scores and also collude with other peers to make themselves or others appear trustworthy. In this paper, we propose the use of outlier detection technique to detect false testimony as outliers. We have applied rough set theory, an efficient and intelligent mathematical tool, to detect the outliers in the trust scores. We present the detailed methodology for implementing rough set theory for P2P network and detecting outlier scores in reputation metrics given by other peers and compared the model with the mechanism to detect outliers with the Eigen Trust model and eBay system. Trust computation without the outlier scores would be more accurate and enable proper verification and evaluation of peer trustworthiness. Copyright (c) 2013 John Wiley & Sons, Ltd.