BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Identifying and discriminating between web and peer-to-peer traffic in the network core
Proceedings of the 16th international conference on World Wide Web
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Bayesian Neural Networks for Internet Traffic Classification
IEEE Transactions on Neural Networks
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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 errorcorrecting 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 data sets captured on the backbone router of our campus through a week.