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基于DPI和机器学习的网络流量分类方法 被引量：3

A novel method for network traffic classification based on DPI and machine learning
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摘要 网络流量分类是实现网络管理的重要技术之一,但是单一的基于DPI或是机器学习的分类方法分类精确度低。提出了一种基于DPI和机器学习相结合的网络流量分类方法。该方法采用DPI检测已知特征的网络流量,利用机器学习方法辅助分析未知特征以及加密的网络流。实验表明该方法能够提高网络流量分类的精确度。 Network traffic classification is one of the important technology to implement network management,but the method for network traffic classification based on single DPI or machine learning is very poor.An algorithm based on DPI with machine learning for network traffic classification was proposed.Unencrypted network traffic was detected by DPI and others classified by maching learning.The experimental result shows that this method can get a more accuracy classification result.
作者 李国平 王勇 陶晓玲
机构地区 桂林电子科技大学计算机科学与工程学院 桂林电子科技大学信息与通信学院
出处 《桂林电子科技大学学报》 2012年第2期140-144,共5页 Journal of Guilin University of Electronic Technology
基金 国家自然科学基金(61163058) 广西自然科学基金(2011GXNSFB018076)
关键词 流量分类 深度包检测 机器学习 朴素贝叶斯 network traffic classification deep packet inspection machine learning Nave Bayesian
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献9

  • 1胡婷,王勇,陶晓玲.网络流量分类方法的比较研究[J].桂林电子科技大学学报,2010,30(3):216-219. 被引量：4
  • 2Moore A W, Papagiannakik. Toward the accurate identi- fication of network applications [C]//PAM 2005 Pro- ceedings of the 6th Intenational Workshop on Passive and Active Network Measurement. Berlin: Springer-Verlag, 2005:41-54.
  • 3Sen S, Spatscheck O, Wang D. Accurate, scalable inter- net-work idetification of P2P traffic using application signatures [C]//Proceedings of the 13th International Conference on World Wide Web. New York: [ s. n. ], 2004.
  • 4Zhao Honglai,Galis A,Rio M, et al. Towards automatic traffic elassification[C]//Third International Conference on Networking and Services. Athens :[ s. n. ], 2007 : 19- 29.
  • 5Zander S, Nguyen T, Armitage G. Automated traffic classification and application identification using ma- chine learning[C]//Proeeedings of the IEEE Confer- ence on Local Computer Network 30th Anniversary. Sydney:IEEE Press, 2005 : 250-257.
  • 6Moore A W,Zuev D. Internet traffic classification using Bayesian analysis techniques[C]//Preceedings of the ACM SIGMETRICS Internet Conference on Measure- ment and Modeling of Computer Systems. New York: ACM Press,2005:50-60.
  • 7徐鹏,林森.基于C4.5决策树的流量分类方法[J].软件学报,2009,20(10):2692-2704. 被引量：171
  • 8Karagiannis T, Broido A, Faloutsos M, et al. Transport layer identification of P2P traffic[C]//Proc of the 4th ACM SIGCOMM Conference on Internet Measurement. New York: ACM Press, 2004 : 121-134.
  • 9Kumar S, Turner J, Williams J. Advanced algorithms for fast and scalable deep packet inspeetionEC-]//Proc of IEEE/ACMANC'06. New York: ACM Press, 2006 : 81-92.

二级参考文献37

  • 1Moore AW, Zuev D. Internet traffic classification using Bayesian analysis techniques. In: Proc. of the 2005 ACM SIGMETRICS Int'l Conf. on Measurement and Modeling of Computer Systems, Banff, 2005. 50-60. http://www.cl.cam.ac.uk/-awm22 /publications/moore2005internet.pdf.
  • 2Madhukar A, Williamson C. A longitudinal study of P2P traffic classification. In: Proc. of the 14th IEEE Int'l Syrup. on Modeling, Analysis, and Simulation. Monterey, 2006. http://ieeexplore.ieee.org/xpl/ffeeabs_all.jsp?arnumber=1698549.
  • 3Moore AW, Papagiannaki K. Toward the accurate identification of network applications. In: Dovrolis C, ed. Proc. of the PAM 2005. LNCS 3431, Heidelberg: Springer-Verlag, 2005.41-54.
  • 4Karagiannis T, Papagiannaki K, Faloutsos M. BLINC: Multilevel traffic classification in the dark. In: Proc. of the ACM SIGCOMM. Philadelphia, 2005. 229-240. http://conferences.sigcomm.org/sigcomm/2005/paper-KarPap.pdf.
  • 5Roughan M, Sen S, Spatscheck O, Dutfield N. Class-of-Service mapping for QoS: A statistical signature-based approach to IP traffic classification. In: Proc. of the ACM SIGCOMM Internet Measurement Conf. Taormina, 2004. 135-148. http://www.imconf.net/imc-2004/papers/p 135-roughan.pdf.
  • 6Zuev D, Moore AW. Traffic classification using a statistical approach. In: Dovrolis C, ed. Proc. of the PAM 2005. LNCS 3431, Heidelberg: Springer-Verlag, 2005. 321-324.
  • 7Nguyen T, Armitage G. Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks. In: Proc. of the 31 st IEEE LCN 2006. Tampa, 2006. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4116573.
  • 8Eerman J, Mahanti A, Arlitt M. Internct traffic identification using machine learning techniques. In: Proc. of the 49th IEEE GLOBECOM. San Francisco, 2006. http://pages.cpsc.ucalgary.ca/-mahanti/papers/globecom06.pdf.
  • 9Erman J, Arlitt M, Mahanti A. Traffic classification using clustering algorithms. In: Proc. of the ACM SIGCOMM Workshop on Mining Network Data (MineNet). Pisa, 2006. http://conferences.sigcomm.org/sigcomm/2006/papers/minenet-01.pdf.
  • 10Bernaille L, Teixeira R, Salamatian K. Early application identification. In: Proc. of the Conf. on Future Networking Technologies 2006 (CoNEXT 2006). Lisboa, 2006. http://portal.acm.org/citation.efm?id=1368445.

共引文献173

同被引文献20

引证文献3

二级引证文献9

桂林电子科技大学学报
2012年 第2期

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