On traffic classification and its applications in the Internet

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Doctoral thesis (monograph)
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Report / Helsinki University of Technoloy, Networking Laboratory, Raportti / Teknillinen korkeakoulu, Tietoverkkolaboratorio, 3/2005
In this work, the methods and applications of traffic classification in the Internet are examined in detail. First, we define and discuss the conceptual environment of traffic classification. We then discuss the performance issues of traffic classification and define a method of visualization to compare the performance of traffic classification implementations. Previously introduced methods of traffic classification: the static applications, the packet count and the list classifiers are compared with each other. We find these methods to perform quite well when analyzed as performing in an IP router, but to be rather ambiguous as to the effect they cause to the user. We introduce an implementation of dynamic traffic classification to two classes using learning vector quantization (LVQ) for flow analysis data and find it to perform well in a simulated environment using flow analysis made on traffic measurements. In comparison to the previous methods of traffic classification, we see that the LVQ classifier has adequate performance. We also study a method of traffic classification using consecutive flow analysis with varying values of the parameters of the flow and find that we are able to classify traffic to 2 or 3 different classes. Within the classes the applications are similar in measured behavior and thus may provide help in realizing some advanced Internet service architectures. Finally, we also observe the application of the dynamic classifier in an Internet router and in the Internet itself. We argue that the implementation of the dynamic classification method is feasible in the network.
traffic measurements, traffic classification, Internet, neural networks
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