Traffic identification using flexible neural trees

Traditional traffic classification techniques like port-based and payload-based techniques are becoming ineffective owning to more and more Internet applications using dynamic port number and encryption techniques. Therefore, in the past few years, many researches have addressed machine learning-based techniques. Most researches of machine learning-based traffic identification use traffic samples collected on key nodes of networks for their learning. These samples do not have accurate application information i. e. the ground truth which is crucial for machine learning algorithms. In this paper, we first designed a distributed host based traffic collecting platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then we built a data set using DHTCP, and applied Flexible Neural Trees (FNT) - a special kind of artificial neural network which has been successfully applied in many areas, for traffic identification. Web and P2P traffics were studied in our work. Although the proposed technique is at an early stage of development, experimental results show that it is a promising solution of Internet traffic identification.