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基于复杂网络社团划分的网络流量分类 被引量：5

Internet Traffic Classification Based on Detecting Community Structure in Complex Network
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摘要 随着网络的高速发展以及各种应用的不断涌现,采用端口号映射或有效负载分析的方法进行流量分类与应用识别已难以满足应用的需求。以流为网络节点、流之间统计特征的相似度为边,构建流相关网络模型,利用New-man快速社团划分算法(NFCD)对流相关网络模型进行社团划分,得到了流的聚类结果,实现了网络流量的分类,并与先前的两种无监督的流量分类算法(K-Means,DBSCAN)进行了对比。实验结果显示,利用NFCD算法具有更高的准确率,并能产生更好的聚类效果,且不受输入参数影响。 In recent years,Internet traffic classification using port-based or payload-based methods is becoming increa-singly difficult with peer-to-peer（P2P） applications using dynamic port numbers,masquerading techniques,and encryption to avoid detection.Because supervised clustering algorithm needs accuracy of training sets and it can not classify unknown application,we introduced complex network＇s community detecting algorithm,a new unsupervised classify algorithm,which has previously not been used for network traffic classification.We evaluated this algorithm and compared it with the previously used unsupervised K-means and DBSCAN algorithm,using empirical Internet traces.The experiment results show complex network＇s community detecting algorithm works very well in accuracy and produces better clusters,besides,complex network＇s community detecting algorithm need not know the number of the traffic application beforehand.
作者 蔡君 余顺争
机构地区 中山大学电子与通信工程系 广东技术师范学院电子与信息学院
出处 《计算机科学》 CSCD 北大核心 2011年第3期80-82,86,共4页 Computer Science
基金 国家高技术研究发展计划(863)(2007AA01Z449) 国家自然科学基金(60970146) 国家自然科学基金-广东联合基金重点项目(U0735002)资助
关键词 流量分类 无监督聚类 社团划分 复杂网络 Traffic classification Unsupervised clustering Community detecting algorithm Complex network
分类号 TN919.26 [电子电信—通信与信息系统]
  • 相关文献

参考文献15

  • 1Sen S,Spatscheck O, Wang Dongmei. Accurate, scalable in network identification of p2p traffic using application signature[C]// Proceedings of the 13th international conference on World Wide Web. 2004,512-521.
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二级参考文献17

  • 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.
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  • 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.
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  • 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.
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共引文献170

同被引文献31

  • 1屠金路,金瑜,王庭照.bootstrap法在合成分数信度区间估计中的应用[J].心理科学,2005,28(5):1199-1200. 被引量：12
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  • 6KARAGIANNIS T, PAPAGIANNAKI D, FALOUTSOS M. BLINC: Multilevel traffic classification in the dark [ J]. Computer Communi- cation Review, 2005, 35(4): 229-240.
  • 7DEWAELE G, HIMURA Y, BORGNAT P. Unsupervised host be- havior classification from connection patterns [ J]. International Jour- nal of Network Management, 2010, 20(5) : 317 -337.
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引证文献5

二级引证文献11

计算机科学
2011年 第3期

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