Traffic flow monitoring in software-defined network using modified recursive learning

Abstract

The recursive network design used in this paper to monitor traffic flow ensures accurate anomaly identification. The suggested method enhances the effectiveness of cyber attacks in SDN. The suggested model achieves a remarkable attack detection performance in the case of distributed denial-of-service (DDoS) attacks by preventing network forwarding performance degradation. The suggested methodology is designed to teach users how to match traffic flows in ways that increase granularity while proactively protecting the SDN data plane from overload. The application of a learnt traffic flow matching control policy makes it possible to obtain the best traffic data for detecting abnormalities obtained during runtime, improving the performance of cyber-attack detection. The accuracy of the suggested model is superior to the MMOS, FMS, DATA, Q-DATA, and DEEP-MC by 19.23%, 16.25%, 47.61%, 16.25%, and 12.04%.