Earticle

초록

영어

This paper focuses on the sampling-based Deep Packet Inspection for the traffic of P2P file sharing systems, especially for BitTorrent, and proposes a logarithmic-based Adaptive Trust Sampling (ATS) strategy for P2P traffic identification. In the whole process of sampling identification for P2P traffic, the sampling ratio of the current node in a P2P network can automatically adjust and dynamically vary according to the estimator of P2P traffic ratio of historical cycles. The experimental results show that the Adaptive Trust Sampling strategy can adapt to the dynamic change of sample size, effectively reduce the total sample size, mitigate the consumptions of system resources to some extent, and achieve the purpose of P2P traffic sampling.

목차

Abstract
 1. Introduction
 2. System Environment and Proposed Framework
  2.1. System Deployment Environment for P2P Traffic Inspection
  2.2. An Adaptive Trust Sampling Framework for P2P Traffic Inspection
 3. Adaptive Trust Sampling Strategy and Algorithm
  3.1. Sampling Strategies
  3.2. An Adaptive Trust Sampling Strategy
  3.3. An Adaptive Trust Sampling Algorithm
 4. Experiment Results and Analyses
  4.1. Experimental Environment
  4.2. Sampling Results and Analyses
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Hongwei Chen [ School of Computer Science, Hubei University of Technology, Wuhan, China ]
• Dongyang Yu [ School of Computer Science, Hubei University of Technology, Wuhan, China ]
• Chunzhi Wang [ School of Computer Science, Hubei University of Technology, Wuhan, China ]
• Shuping Wang [ School of Computer Science, Hubei University of Technology, Wuhan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605