Network Traffic Classification Based on Error-Correcting Output Codes and NN Ensemble

Classification of network traffic is basic and essential for many network researches and managements. However, classification of network traffic using port-based and simple payload-based methods is diminished with the rapid development of peer-to-peer (P2P) application using dynamic port, disguising techniques and encryption to avoid detection. An alternative method based on statistics and machine learning has attracted researchers´ attention in recent years. In this paper, a new approach based on the implementation of artificial neural network ensemble with the error-correcting output codes (ECOC) is proposed for classification of multi-class network traffic. As the error-correcting output codes have error correcting ability and improve the generalization ability of the base classifiers the experiments show that the proposed method can improve the multi-class classification accuracy by 12%-20% on datasets captured on the backbone router of our campus through a week.