A Classification Algorithm for Network Traffic based on Improved Support Vector Machine
A Classification Algorithm for Network Traffic based on Improved Support Vector Machine
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DOI：
10.4304/jcp.8.4.1090-1096
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Lei Ding;Fei Yu;S. Peng;Chen Xu
中科院分区：
文献类型：
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作者：
Lei Ding;Fei Yu;S. Peng;Chen Xu
提出一种基于改进支持向量机(SVM)的网络流量分类算法。传统支持向量机（SVM）算法的每个特征对分类的效果都是相同的，而不考虑其实际效果。为了提高SVM的分类精度，从真实的网络流量中获取某一类网络流量中某个特征的概率分布区域。然后计算特征在两种不同类型网络流量之间的概率分布区域的重叠程度，得到特征的贡献度，并根据其贡献度得出特征对应的权重值。因此每个特征根据其权重值对分类有不同的影响。考虑到特征的概率分布区域受到异常值或噪声的强烈影响，将数据空间映射到高维特征空间，并采用Gustafson-Kessel聚类算法处理输入样本中存在的异常值或噪声。实验结果表明本文提出的方法具有较高的分类精度。
An algorithm to classify the network traffic based on improved support vector machine (SVM) is presented in this paper. Each feature of the traditional support vector machine (SVM) algorithm has the same effect on classification rather than considering its practical effect. To improve the classification accuracy of SVM, the probabilistic distributing area of a feature in a kind of network traffic is obtained from the real network traffic. Then the overlapped degree of the feature’s probabilistic distributing area between two different kinds of network traffic is calculated to obtain the feature’s contribution degree, and the corresponding weight value of the feature is derived from its contribution degree. Thus each feature has different effect on the classification according to its weight value. Considering the feature’s probabilistic distributing area is affected by the outliers or noises intensively, the data space is mapped to high dimension feature space, and the Gustafson-Kessel clustering algorithm is employed to deal with the outliers or noises existing in the input samples. The experimental results show that the method presented in this paper has a higher classification accuracy.