Cor-ENTC:correlation with ensembled approach for network traffic classification using SDN technology for future networks

According to a study on advanced technology, resource planning, and design for Fifth Generation (5G) architecture and elsewhere, network traffic classification is a basic and complex part of software-defined networking (SDN). 5G requires end to-end security that uses its software-defined architecture to automatically monitor the network and classify the traffic flows. To improve the security in network traffic classification, it is necessary to apply a correct set of policy rules to classify future network traffic flows. To create a communication-efficient and intelligent traffic classification framework in the SDN environment, machine learning is used between the data and the control planes. The proposed ensemble learning pre-processing tool to categorize incoming VPN traffic by applying a possible set of policies. The proposed technique is compared with existing classifiers and has a higher accuracy of 98% to 99.9% for ensemble models than single classifiers and other existing options, according to an evaluation of performance.