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基于半监督学习的网络流量分类 被引量：5

Network Traffic Classification Based on Semi-supervised Learning
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摘要 利用攻击在网络通信中独特的流特征,给出一个可以适应已知和未知攻击的半监督分类方法。在训练分类器中,提出使用加权采样技术得到训练流,同时采用顺序前向选择算法得到最佳的特征子集。使用KDDCUP1999性能评估数据,可以得到较高的流和字节分类准确度。 This paper exploits distinctive flow characteristics of attacks when they communicate on a network, and proposes a semi-supervised classification method that can accommodate both known and unknown attacks. In training the classifier, it employs Sequential Forward Selection（SFS） to get the best feature subset. Meanwhile, it proposes weighted sampling techniques to obtain training flows. Performance evaluation using KDD CUP1999 data shows that high flow and byte classification accuracy can be achieved.
作者 佘锋 王小玲
机构地区 中南大学信息科学与工程学院
出处 《计算机工程》 CAS CSCD 北大核心 2009年第12期90-91,94,共3页 Computer Engineering
基金 国家自然科学基金资助项目(60773013)
关键词 网络流量分类 半监督学习 模糊C均值 入侵检测 network traffic classification semi-supervised teaming Fuzzy C-Means（FCM） intrusion detection
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献6

  • 1Moore A W, Zuev D. Intemet Traffic Classification Using Bayesian Analysis Techniques[J]. Performance Evaluation, 2005, 33(1): 50-60.
  • 2Erman J, Mahanti A, Arlitt M, et al. Offline/Realtime Traffic Classification Using Semi-supervised Learning[J]. Performance Evaluation, 2007, 64(9-12): 1194-1213.
  • 3Hettich S, Bay S D. The UCI KDD Archive[EB/OL]. (1999-10-20). http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
  • 4Dy J G, Brodley C E. Feature Selection for Unsupervised Learning[J]. The Journal of Machine Learning Research, 2004, 5(1): 845-889.
  • 5Pal N R, Bezdek J C. On Clustering for the Fuzzy C-means Model[J]. Proc. of the IEEE, 1995, 31(3): 370-379.
  • 6Mori T, Uchida M, Kawahara R, et al. Identifying Elephant Flows Through Periodically Sampled Packets[C]//Proc. of IMC'04. Taormina, Italy: [s. n.], 2004: 115-120.

同被引文献40

  • 1朱明.数据挖掘[M].合肥:中国科学技术大学出版社,2008.
  • 2MOORE A W,PAPAGIANNAKI K.Toward the accurate identification of network applications[C] //Proc.of the 6th International Workshop on Passive and Active Network Measurement.Heidelberg:Springer Verlag,2005:41-54.
  • 3LI Wei,CANINI M,MOORE A W.Efficient application identification and the temporal and spatial stability of classification schema[J].Computer Networks,2009,53(6):790-809.
  • 4CHOI K,CHOI K J.Pattern Matching of Packet Payload for Network Traffic Classification[C] //Proc.of the 1st International Conference on Next Generation Network (NGNCON 2006).Korea:Hyatt Regency Jeju,2006.
  • 5ZANDER S,NGUYEN T,ARMITAGE G.Automated Traffic Classification and Application Identification using Machine Learning[C] //Proc.of the IEEE Conference on Local Computer Networks 30th Anniversary,2005:250-257.
  • 6MOORE A W,ZUEV D.Internet traffic classification using Bayesian analysis techniques[C] //Proc.of ACM SIGMETRICS,New York:ACM Press,2005:50-60.
  • 7ERMAN J,MAHANTI A,ARLITT M,COHEN I.Offline/ realtime traffic classification using semi-supervised learning[J].Performance Evaluation,2007,64(9-12):1194-1213.
  • 8DAI Lei,YUN Xiaochun,XIAO Jun.Optimizing Traffic Classification Using Hybrid Feature Selection[C] //Proc.of the Ninth International Conference on Web-Age Information Management,2008.
  • 9LI Zhu,YUAN Ruixi,GUAN Xiaohong.Accurate Classification of the Internet Traffic Based on the SVM Method[C] //Proc.of IEEE International Conference on Communications(ICC),2007:1373-1378.
  • 10MA Yongli,QIAN Zongjue,SHOU Guochu.Study on Preliminary Performance of Algorithms for Network Traffic Identification[C] //Proc.of 2008 International Conference on Computer Science and Software Engineering,2008,1:629-633.

引证文献5

二级引证文献11

计算机工程
2009年 第12期

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