Network traffic classification method based on kernel self-organizing maps
Due to network traffic is highly nonlinear, classical self-organizing maps (SOM) is worse robustness and reliability because it adopts Euclidean distance. A network traffic classification method named kernel-SOM (KSOM) is proposed, which adopts kernel function to replace Euclidean distance. This method can simplify the complicated flow sample from input space to feature space, so achieve good classification of network traffic that has several statistic feature attributes in application layer. Experimental results demonstrate that KSOM can identify flows which represent new application protocol. This method has more excellent performance than traditional SOM, and achieves higher classify accuracy than NB algorithm.
NETL
NSTL
万方数据
维普
