  The continual growth of high speed networks is a challenge for real-time
network analysis systems. The real time traffic classification is an issue for
corporations and ISPs (Internet Service Providers). This work presents the
design and implementation of a real time flow-based network traffic
classification system. The classifier monitor acts as a pipeline consisting of
three modules: packet capture and pre-processing, flow reassembly, and
classification with Machine Learning (ML). The modules are built as concurrent
processes with well defined data interfaces between them so that any module can
be improved and updated independently. In this pipeline, the flow reassembly
function becomes the bottleneck of the performance. In this implementation, was
used a efficient method of reassembly which results in a average delivery delay
of 0.49 seconds, approximately. For the classification module, the performances
of the K-Nearest Neighbor (KNN), C4.5 Decision Tree, Naive Bayes (NB), Flexible
Naive Bayes (FNB) and AdaBoost Ensemble Learning Algorithm are compared in
order to validate our approach.
