Research of sampling method applied to traffic classification

For the purpose of reducing computational complexity and improving classification accuracy, we proposed an efficient method which applies mask-match sampling to machine learning for traffic classification. By picking an optimal sampling rate, the overhead for capturing flow characteristics is greatly reduced, while maintaining the traffic pattern. It is more suitable for today´s high-speed network traffic classification.