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。

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.