A Machine Learning Approach for Efficient Traffic Classification

Today's talk on the Computer Lab was on "A Machine Learning Approach for Efficient Traffic Classification". The speaker was Wei Li. Wei Li was a PhD student in QMUL but now has moved to the Computer Lab "following [his] supervisor Andrew Moore".

The talk was on the very interesting subject of online traffic classification. Besides being used for network monitoring and intrusion detection, traffic classification can also serve as the input for application modeling. For that reason it could be useful to model the IPTV service, for instance, which would in turn be important as an input to my OPNET simulations.

Wei Li presented a machine-learning approach that accurately classifies internet traffic using C4.5 decision tree. Without inspecting packet payload, their method can identify traffic of different types of applications with 99.8% total accuracy, by collecting 12 features at the start of the flows. But overall it was a very nice talk.

Some questions arose on the features chosen (why those 12 and not others, when 248 were available?) and on the relationship between accuracy and time (can be an important aspect to consider in some applications, like intrusion detection).