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基于层次聚类的网络流识别算法研究

丁 伟,徐 杰,卓文辉   

  1. 1.东南大学 计算机科学与工程学院,江苏 南京 211189; 2. 东南大学 计算机网络与信息集成教育部重点实验室,江苏 南京 211189
  • 出版日期:2014-10-25 发布日期:2014-12-16

Net traffic identifier based on hierarchical clustering

  • Online:2014-10-25 Published:2014-12-16

摘要: 利用核函数定理提出了一种改进的网络流识别算法。首先运用对称不确定性的概念选择出最相关的流测度,然后利用核函数定理对选择的网络流测度进行高维映射,以测度的高维空间距离作为度量各个类差别的标准,提高了聚类结果的准确性。采用光滑因子、轮廓系数和不确定熵来控制聚类过程。实验表明,该算法的聚类结果更均匀,没有出现某个类占过大比重的情况且根据高维空间的类距离能够检测出网络流里的大部分流量。

Abstract: An improved net traffic identifier algorithm was proposed based on semi-supervised clustering. Symmetrical uncertainty was used to reduce the net flow attributes, and then kernel function was used to project the rest attributes to higher dimentional space. The train net flow was clustered in high dimentional space hierarchically. Smooth factor, sihouette coefficient and entropy controlled the cluster process to get a well result. Experiments show that the algorithm got flat clusters without any huge cluster and could identify most net flow even encrypted ones.

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