Network traffic classification based on Kernel Self-Organizing Maps

Network traffic classification has always been an important part of realizing effective network management. Due to network traffic is high-dimensional nonlinear, classical Self-Organizing Maps (SOM) has poor robustness and reliability because it adopts Euclidean distance. A network traffic classification method based on Kernel-SOM (KSOM) is proposed, which replaces Euclidean distance with non-Euclidean distance induced by kernel function, and adopts it to estimate the matching degree between the input pattern and the connection weight. Experimental results demonstrate that compared with the classical SOM and NB, KSOM achieves higher classification precision, and has shown fascinating characteristic when being used in the classification of network traffic.