GPU-oriented stream data mining traffic classification

Network Management depends on precise characterization of the traffic profile of networked applications. When the identification and classification of network flows is done using machine learning, the characterization of traffic still requires an approach that is capable of providing a balance between accuracy and processing speed in real-time scenarios. This paper proposes an architecture to classify network traffic based on Stream Data Mining techniques using Graphic Processing Units (GPU), in order to meet the requirements of both classification accuracy and speed. Our proposal combines the characteristics of data mining techniques with a continuous stream of input data, and with high processing performance GPU architecture. Results show that our approach provides accuracy comparable to or better than existing related work (e.g., above 95%) while ramping up performance (e.g., up to 62x speed up), comparing the different implementations of our approach. These facts allow the deployment of the proposed technique to the real-time management of high speed backbone links.