Traffic data classification in SDN network based on machine learning algorithms

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Traffic classification plays a crucial role in various domains of network management, including service measurement, architectural design, security monitoring, and advertising. Software-defined networks (SDN) is a new technology that has the potential to solve typical network problems by simplifying network management, the introduction of network programmability, and the provision of a global perspective of a network. In recent years, SDN has brought new opportunities to classify traffic. Traffic classification techniques in SDN have been investigated, proposed, and developed. This survey delves into traffic classification under SDN, which is a vital component for improving network services, administration, and security. We give an in-depth assessment of traffic categorization algorithms adapted for SDN, emphasizing the fresh opportunities and problems they present. We cover the many metrics for assessing the effectiveness of these traffic classification algorithms, such as accuracy, precision, recall, and F1 score, and we examine the numerous datasets that serve as performance benchmarks. The study also synthesizes the findings of existing research, revealing trends and the efficacy of various techniques in the context of SDN-enabled settings. This document serves as a resource for scholars and practitioners seeking to optimize traffic classification strategies by providing a complete review and assessment of existing traffic classification approaches