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网络流量分类研究进展与展望 被引量:23

Research Progress and Prospects of Network Traffic Classification
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摘要 近年来,随着互联网的迅猛发展,越来越多的新型网络应用逐渐兴起,网络规模不断扩大,网络组成也越来越复杂。网络流量分类技术作为增强网络可控性的基础技术之一,不仅可以帮助网络运营商提供更好的服务质量,而且能够对网络进行有效的监督管理,确保网络安全。本文综述了网络流量分类领域的研究方法及研究成果,对这些传统方法进行比较,分别指出它们的优势和不足。并针对高速网络环境下的实时分类、加密流分类、精细化分类、协议动态变化时的分类等现实挑战,对相关研究进展进行阐述和分析。最后对未来的研究方向进行展望。 In recent years,the number of applications and the scalability of the Internet have experienced a rapid improvement.As one of the basic technologies for enhancing network controllability,traffic classification can not only provide better QoS for ISPs,but also supervise and manage network effectively,which can ensure the security of the Internet.In this paper,we first review the methodology and achievements in the field of traffic classification by comparing these traditional methods,and pointing out their advantages and disadvantages.Then we explain and analyze the related research progress aiming at challenges in reality such as real-time classification in backbone network,encrypted traffic classification,fine-grained classification,and constantly changing protocols classification etc.Finally,we look into the future of our research.
出处 《集成技术》 2012年第1期32-42,共11页 Journal of Integration Technology
基金 国家高技术研究发展计划("863"计划)(2011AA010703) 国家自然科学基金项目(61070184)资助项目
关键词 流量分类 高速网络 精细化 加密 协议混淆 traffic classification high-speed network fine-grained encryption protocol obfuscation
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  • 1Madhukar A, Williamson C. A longitudinal study of P2P traffic classification [C]//Proc of the 14th IEEE Int Syrup on Modeling, Analysis, and Simulation. Washington, DC IEEE Computer Society, 2006:179-188
  • 2Moore A W, Papagiannaki K. Toward the accurate identification of network applications [G]//Dovrolis C. LNCS 3431: Proc of the PAM 2005. Heidelberg: Springer, 2005:41-54
  • 3Karagiannis T, Papagiannaki K, Faloutsos M. BLINC: Multilevel traffic classification in the dark [C]//Proc of ACM SIGCOMM. New York: ACM, 2005.. 229-240
  • 4Roughan M, Sen S, Spatscheck O, et al. Class of service mapping for QoS: A statistical signature-hased approach to IP traffic classification [C]//Proc of ACM SIGCOMM Internet Measurement Conf 2004. New York: ACM, 2004: 135-148
  • 5Zuev D. Moore A W. Traffic classification using a statistical approach [G]//Dovrolis C. LNCS 3431: Proc of the PAM. Heidelberg, Germany: Springer, 2005:321-324
  • 6Moore A W, Zuev D. Internet traffic classification using Bayesian analysis techniques [C] //Proc of the 2005 ACM SIGMETRICS Int Conf on Measurement and Modeling of Computer Systems. New York: ACM, 2005: 50-60
  • 7Tan P N, Steinbach M, Kumar V. Introduction to Data Mining [M]. Boston: Addison Wesley, 2006
  • 8Moore A W, Zuev D, Crogan M. Discriminators for use in flow-based classification, RR-05-13 [R]. London: Queen Mary University of London, 2005
  • 9Witten I H, Frank E. Data Mining: Practical Machine Learning Tools and Techniques [M]. 2nd ed. Amsterdam: Elsevier Inc. , 2005
  • 10Chang C C, Lin C J. LIBSVM: A library for support vector machines[EB/OL]. 2001 [2007-08-06]. http://www.csie. ntu. edu. tw/-ejlin/libsvm

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