SD Network based on Machine Learning: An Overview of Applications and Solutions

Abstract

The integration of Software-Defined Networking (SDN) and Machine Learning (ML) provides a promising framework for creating adaptive, secure, and responsive networks. This method allows for resource allocation, traffic routing, and security optimization by fusing the centralized control structure of SDN with the data-driven insights of machine learning. This review assesses important studies in SDN-ML applications, emphasizing both important contributions and noteworthy drawbacks, such as limited experimental validation, scalability, and problems with data quality. Future research should investigate sophisticated machine learning techniques, provide scalable frameworks, and improve dataset quality in order to tackle these issues. This study demonstrates how SDN-ML integration may be used to build network environments that are secure, intelligent, and responsive.

Keywords- SD Network based, Machine Learning, Applications and Solutions