Traffic Classification in Software-Defined Networking by Employing Deep Learning Techniques: A Systematic Literature Review

Traffic Classification in Software-Defined Networking by Employing Deep Learning Techniques: A Systematic Literature Review

연구 분야: Networking

학회: International Conference on Technologies and Innovation

Software-Defined Networking provides a global vision of the network, centralized controller, dynamic routing, dynamic update of the flow table, and traffic analysis. The features of Software-Defined Networking and the integration of Deep Learning techniques allow the introduction of intelligence to optimize, manage and maintain them better. In this context, this work aims to provide a Systematic Literature Review on traffic classification in Software-Defined Networking with Deep Learning techniques. Furthermore, we analyze and synthesize the selected studies based on the categorization of traffic classes and the employed Deep Learning techniques to draw meaningful research conclusions. Finally, we identify new challenges and future research directions on this topic.