Network Traffic Classification using Machine Learning for Software Defined Networks

Menuka Perera (1, 2) , Kandaraj Piamrat (2, 1) , Salima Hamma (1, 2)

1 LS2N - équipe RIO - Réseaux pour l'Internet des Objets
2 LS2N - Laboratoire des Sciences du Numérique de Nantes

The recent development in industry automation and connected devices made a huge demand for network resources. Traditional Networks are becoming less effective to handle this large number of traffic generated by these technologies. At the same time, Software defined networking (SDN) introduced a programmable and scalable networking solution that enables Machine Learning (ML) applications to automate networks. Issues with traditional methods to classify network traffic and allocate resources can be solved by this SDN solution. Network data gathered by the SDN controller will allow data analytics methods to analyze and apply machine learning models to customize the network management. This work has focused on analyzing network data and implement a network traffic classification solution using machine learning and integrate the model in SDN platform.

Mots clés: Machine Learning; Classification; Network Traffic; Software- Defined Networking

Domaines: Réseaux et télécommunications [cs.NI]; Apprentissage [cs.LG]

Menuka Perera, Kandaraj Piamrat, Salima Hamma. Network Traffic Classification using Machine Learning for Software Defined Networks. Journées non thématiques GDR-RSD 2020, Jan 2020, Nantes, France. ⟨hal-02539341⟩