Classification of Peer-to-Peer traffic using incremental neural networks (Fuzzy ARTMAP)

We present application of data mining, and in particular, fuzzy ARTMAP neural networks, in classification of peer-to-peer (P2P) traffic in IP networks. We captured Internet traffic at a main gateway router, performed pre-processing on the data, selected the most significant attributes, and prepared a training data set to which the fuzzy ARTMAP algorithms were applied. Fuzzy ARTMAP is an incremental learning classifier suitable for mining stream of data. We built several models using incremental and non-incremental approaches for different sizes of the training data set. We observed that when the size of the training set is relatively small, incremental learning has better performance than non-incremental algorithm. This highlights the efficiency of the incremental learning classifier in stream data mining applications where memory size is usually limited. Our approach relies only on the IP header of the packets, eliminating the privacy concern associated with the techniques that use deep packet inspection.