Low complexity, high performance neuro-fuzzy system for Internet traffic flows early

Traffic flow classification to identify applications and activity of users is widely studied both to understand privacy threats and to support network functions such as usage policies and QoS. For those needs, real time classification is required and classifier's complexity is as important as accuracy, especially given the increasing link speeds also in the access section of the network. We propose the application of a highly efficient classification system, specifically Min-Max neurofuzzy networks trained by PARC algorithm, showing that it achieves very high accuracy, in line with the best performing algorithms on Weka, by considering two traffic data sets collected in different epochs and places. It turns out that required classification model complexity is much lower with Min-Max networks with respect to SVM models, enabling the implementation of effective classification algorithms in real time on inexpensive platforms.