Internet traffic classification using feed-forward neural network

Many network activities can benefit from accurate traffic classification and categorization, such as QOS control, network security monitoring, and traffic accounting. In this paper, a new approach based on feed-forward neural network is proposed for accurate traffic classification, which eliminates the disadvantages of port-based or payload-based classification methods. Extensive experimentation and comparison have been carried out to explore this new approach; it has been found out that, combined with a fast correlation-based feature selection filter, better performance and more accurate classification results can be obtained using neural network method compared to other techniques. For its good performance and elimination of accessing the contents of the packets, the proposed technique is expected to have a promising application prospect in internet traffic classification.