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Network traffic classification using a parallel neural network classifier architecture

Online traffic classification is an important capability for numerous applications, including network monitoring, QoS (Quality of Service) management, and anomaly detection. In this paper we propose an approach to classify TCP/IP network traffic based on a small set its statistical properties. Our approach relies on a Parallel Neural Network Classifier Architecture (PNNCA) for classification, and has been preliminarily tested for the classification of HTTP traffic in controlled environments. Our initial results have shown a Correct Classification Rate of 85% to 91%, depending on the characteristics of the test sets.