Home > Archive > 2011 > Volume 1 Number 4 (Oct. 2011) >
IJMLC 2011 Vol.1(4): 366-371 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.54

Cluster-Based Reputation Model in Peer-to-Peer Network

Mei Chen, Kenji Kita and Xin Luo

Abstract—Nowadays, Peer-to-Peer network represents a large portion of internet traffic, and becomes fundamental data sources. Because of lacking the security mechanism from third-party, P2P network will face some severe trust problems such as service faking and resource abusing by some malicious peers. The conventional security measures can not be used to cater for this demand, whereas the scenario based on reputation has widely been accepted. Through studying the present reputation, the paper presents a cluster-based reputation model (CBRM). The model is consisted by reputation mechanism and cluster. In the model, we take the reputation mechanism for realizing the security transaction; and the network topology structure of CBRM adopts the cluster, so efficiency of reputation management is noticeably raised. In order to improve security, reduce the network traffic brought by management of reputation, and enhance stability of cluster, when we select reputation, the average historical online time, and the network bandwidth as the elementary components of the comprehensive performance of node. Simulation results showed that the proposed model improved the security, reduced the network traffic, and enhanced stability of cluster.

Index Terms—P2P; reputation; trust; security; cluster; network;;

Mei Chen and Kenji Kita are with the University of Tokushima, Tokushima, Japan. (e-mail: chen-mei@iss.tokushima-u.ac.jp; kita@is.tokushima-u.ac.jp).
Xin Luo is with the School of Computer Science and Technology, Donghua University, Shanghai, China (e-mail: xluo@dhu.edu.cn).

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Cite: Mei Chen, Kenji Kita and Xin Luo, "Cluster-Based Reputation Model in Peer-to-Peer Network," International Journal of Machine Learning and Computing vol. 1, no. 4, pp.366-371, 2011.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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