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Network Traffic Classification Using Machine Learning for Software Defined Networks

Abstract : 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 paper has focused on analyzing network data and implement a network traffic classification solution using machine learning and integrate the model in software-defined networking platform.
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Submitted on : Monday, June 21, 2021 - 5:31:13 PM
Last modification on : Wednesday, April 27, 2022 - 3:42:43 AM
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Menuka Perera Jayasuriya Kuranage, Kandaraj Piamrat, Salima Hamma. Network Traffic Classification Using Machine Learning for Software Defined Networks. 2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.28-39, ⟨10.1007/978-3-030-45778-5_3⟩. ⟨hal-03266452⟩



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