Paper Info
Title
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.
Year
DOI
Venue
2014
10.1109/3PGCIC.2014.95
3PGCIC
Keywords
Field
DocType
reputation-based peer-to-peer systems,reputation,malicious peers clustering,peer-to-peer, malicious peers, framework, reputation,framework,peer-to-peer,p2p systems,malicious peer detection,malicious peers,distributed p2p environment,peer-to-peer computing,peer selection,security of data,entropy,clustering algorithms,algorithm design and analysis,security,topology
Data collection,Internet privacy,Algorithm design,Content delivery,Incentive,Peer-to-peer,Computer science,Computer security,Dead Peer Detection,Cluster analysis,Reputation
Conference
Citations 
PageRank 
References 
0
0.34
22
Authors
4
Name
Order
Citations
PageRank
Xiang-Lin Wei111726.16
Jianhua Fan28812.93
Ming Chen35912.00
Guomin Zhang412315.78