A low complexity real-time Internet traffic flows neuro-fuzzy classifier
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 classifiers 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 MinMax neuro-fuzzy networks trained by PARC algorithm, and compare it with popular classification systems, by considering traffic data sets collected in different epochs and places. We show that MinMax networks achieve high accuracy, in line with the best performing algorithms on Weka (SVM, Random Tree, Random Forest). The required classification model complexity is much lower with MinMax networks with respect to the other models, enabling the implementation of effective classification algorithms in real time on inexpensive platforms.
traffic flow classification classifier complexity neurofuzzy networks features selection genetic algorithms fpga