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首页> 外文会议>Proceedings of 2009 2nd IEEE international conference on broadband network amp; multimedia technology >DATA STREAM MINING BASED REAL-TIME HIGHSPEED TRAFFIC CLASSIFICATION
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DATA STREAM MINING BASED REAL-TIME HIGHSPEED TRAFFIC CLASSIFICATION

机译：基于数据流挖掘的实时高速交通分类

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In current high-speed network, Peer-to-Peer(P2P) applications have overtaken Web applications as the major contribution on the Internet. Thereby, how to identify P2P traffic in real-time accurately and efficiently is a key step for network management. In this paper, we highlight the importance of applying data stream method in traffic classification to achieve real-time P2P traffic identification. We not only introduce a VFDT-based real-time highspeed traffic classification method, but also take thoroughly analysis on how to select a reasonable tie confidence (TieC), minimum gathering flow (MinGF) and category number (CaNum). Meanwhile, analysis has been done to ascertain the packet’s interval which is used to calculate flow’s real-time attribute. Experiment results have shown that when TieC is less than threshold, the larger TieC is, the better accuracy of identification is; when TieC exceeds threshold, decision trees are the same. Concerning MinGF and CaNum, although the smaller both of them are, the better performance of decision tree is, the value of them must be properly set according to requirements of classification system.
机译：在当前的高速网络中，对等（P2P）应用程序已取代Web应用程序成为Internet上的主要贡献。因此，如何实时，准确，高效地识别P2P流量是网络管理的关键步骤。在本文中，我们强调了在流分类中应用数据流方法以实现实时P2P流量识别的重要性。我们不仅介绍了基于VFDT的实时高速交通分类方法，还对如何选择合理的平局置信度（TieC），最小收集流量（MinGF）和类别编号（CaNum）进行了详尽的分析。同时，已经进行了分析以确定数据包的间隔，该间隔用于计算流的实时属性。实验结果表明，当TieC小于阈值时，TieC越大，识别精度越好。当TieC超过阈值时，决策树是相同的。关于MinGF和CaNum，尽管两者都较小，但决策树的性能较好，但必须根据分类系统的要求适当设置它们的值。

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