Software-defined networking (SDN) is a revolutionary approach to designing and managing networks that simplifies the process by separating the control and data planes. Nevertheless, these attributes make SDNs susceptible to security risks. As a result, it is critical to include a network intrusion detection system (NIDS) as a response.  

This article suggests utilizing machine learning models to improve the efficiency of NIDS in SDN systems. More specifically, two benchmark datasets—NSL KDD and UNSW-NB15—are used to create and test machine learning models that aim to improve SDN network security and reduce potential threats.