• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2010, Vol. 32 ›› Issue (10): 23-25.doi: 10.3969/j.issn.1007130X.2010.

• 论文 • 上一篇    下一篇

一种基于混合核函数的SOM网络流量分类方法

陶晓玲1,胡婷2   

  1. (1. 桂林电子科技大学网络中心,广西 桂林 541004;2.桂林电子科技大学计算机与控制学院,广西 桂林 541004)
  • 收稿日期:2010-03-11 修回日期:2010-06-29 出版日期:2010-09-29 发布日期:2010-09-29
  • 作者简介:陶晓玲(1977),女,浙江金华人,硕士,工程师,研究方向为网络安全和网格计算;胡婷,硕士生,研究方向为网络安全和网格计算。
  • 基金资助:

    国家自然科学基金资助项目(60872022)

A Network Traffic Classification Method Based on the MIXKernel SelfOrganizing Maps

TAO Xiaoling1,HU Ting2   

  1. (1.Network Center,Guilin University of Electronic Technology,Guilin 541004;
    2.School of Computer and Control,Guilin University of Electronic Technology,Guilin 541004,China)
  • Received:2010-03-11 Revised:2010-06-29 Online:2010-09-29 Published:2010-09-29

摘要:

由于传统的自组织映射SOM方法对高维、非线性的网络流量数据的分类性能效果不佳,本文引入核方法,提出一种基于混合核函数的SOM(MIXKSOM)网络流量分类方法。该方法结合了全局性和局部性核函数的优点,采用径向基函数和多项式函数线性组合构成的混合核函数代替内积作为距离度量,使输入空间中复杂的流量样本在特征空间得以简化。实验结果表明,采用MIXKSOM方法能较好地对网络流量进行分类,较传统的SOM、采用单一核函数的SOM(KSOM)分类方法性能更好,分类准确率也高于NB方法。

关键词: 流量分类, 自组织映射网络, 核函数

Abstract:

Due to the worse classification performance of classical SelfOrganizing Maps (SOM) for network traffic,a network traffic classification method based on the MIXKernel SelfOrganizing Maps(MIXKSOM) is proposed. Applying a mixed kernel function that is the linear combination of the radial basis function and the polynomial function to replace the Euclidean distance as distance measure, this method can not only combine the advantages of global and local kernel functions, but also simplify the complicated flow sample from the input space to the feature space. The experimental results show that this method has a better performance for classifying network traffic than the classification method based on the traditional SOM and the single kernel function SOM(KSOM), and get a better accuracy rate than NB.

Key words: traffic classification;selforganizing maps network;kernel function