A General Framework for Detecting Malicious Peers in Reputation-Based Peer-to-Peer Systems

Abstract

Constructing an efficient and trustable content delivery community with low cost is the general target for the designers of the Peer-to-Peer (P2P) systems. To achieve this goal, many reputation mechanisms are introduced in recent years to alleviate the blindness during peer selection in distributed P2P environment where malicious peers coexist with honest ones. They indeed provide incentives for peers to contribute more resources to the system, and thus, promote the whole system performance. However, little attention has been paid on how to identify the malicious peers in this situation. In this paper, a general framework is presented for detecting malicious peers in Reputation-based P2P systems. Firstly, the malicious peers are divided into various categories and the problem is formulated. Secondly, the general framework is put forward which mainly contains four steps, i.e. Data collection, data processing, malicious peers detection and malicious peers clustering. Thirdly, an algorithm implementation of this general framework is shown. Finally, the framework's application and its performance evaluation are shown.