A Traffic Classification Method Based on Packet Transport Layer Payload by Ensemble Learning Network traffic classification is an important research topic for computer network, such as QoS detection and admission monitoring. Traditional classification methods, such as portbased and DPI(deep packet inspect)based, are outofdate due to the computational expensiveness and inaccuracy. In this paper, we propose a novel traffic classification approach based on packet transport layer payload by ensemble learning. We use three kinds of base neural networks to form a strong classifier. Each model is trained separately and the final prediction result is decided by weight voting. The raw traffic data are reshaped into the format of sequence and matrix as the input, which avoids the TCP stream feature selection and extraction process. Our approach is applicable to both TCP and UDP, which means that it doesnxe2x80x99t require a distinction between transport layer protocols. The experiment results show that our approach can reach the high accuracy of 96.38%, and is better than the stateoftheart methods based on the same dataset. Besides, our proposed model can select packet samples randomly avoiding tracing the whole stream and the model works well even therexe2x80x99s packet loss and disorder.

Integrating Machine Learning with OfftheShelf Traffic Flow Features for HTTPHTTPS Traffic Classification Accurate traffic classification is a key requirement for different network and security monitoringplanning tools. The evolution of Internet protocols and applications has caused traditional traffic classification approaches to be ineffective in certain cases. Key causes of the inaccuracy include: (i) the increase in the encrypted traffic; (ii) the rise in the usage of dynamic port numbers for different applications; and (iii) multiple applications running over HTTPHTTPS protocols. Traditional solutions for traffic analysis, classification, and measurement fall short in providing visibility in usersxe2x80x99 activities  a key requirement for network and security monitoring tools. In this paper, we evaluate an automatic classifier for encrypted Social media, Video and Audio traffic without relying on particular application layer header fields that can be easily modified. We leverage machine learning algorithms together with the features provided by the wellknown offtheshelf traffic flow exporters. We evaluate the performance of such a system also for generalization (robustness) purposes on different networks. Experimental results show promising performances in terms of generating robust traffic classification on large traffic data when the trained model is moved to different networks.