Earticle

초록

영어

With development of scale, diversity and complexity of network traffic, the drawbacks of traditional machine learning methods on traffic classification is gradually exposed, especially the false positive problem in large-scale real network traffic classification is particularly serious. In this paper, aiming at reducing the false positive rate of network traffic classification, an effective network traffic classification method --- CMM method. CMM method contains three steps, including dividing the training set into clusters, forming sub-classifiers, and classifier integration in accordance with the principle of minimization and maximization. In this paper, we firstly demonstrate the effectiveness of this method in reducing the false positive rate. Secondly, we conduct experiments in large-scale national backbone network, such as the SSL protocol classification and experimental results verify the effectiveness of this method in large-scale the actual network traffic classification.

목차

Abstract
 1. Introduction
 2. Related Works
 3. CMM Method
  3.1. Description of Problem
  3.2. Design Philosophy
  3.3. Division of Training Set and Generation of Sub-Classifier
  3.4. Classifier Integration
  3.5. CMM Traffic Classification Method
 4. Experiment and Analysis
  4.1. Experimental Data
  4.2. Measurements and Metrics
  4.3. Results and Analysis
 5. Conclusion
 6. Acknowledgements
 References

키워드

저자

• Zhang Luoshi [ School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China ]
• Xue Yibo [ Research Inst. of Info. & Tech., Tsinghua University, Beijing 100084, China, Tsinghua National Lab for Information Sci. & Tech., Beijing 100084, China ]
• Bao Yuanyuan [ Research Inst. of Info. & Tech., Tsinghua University, Beijing 100084, 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