MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEMS FOR SDN: AN EMPIRICAL STUDY USING KNIME
Lamiae Boukraa, Siham Essahraui, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi, Redouane Esbai · EDPACS · 2024
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 SDN susceptible to security risks. As a result, it is critical to include a Network Intrusion Detection System (NIDS) as a response. This study suggests utilizing machine learning models to improve the efficiency of NIDS in SDN systems. More specifically, we use two benchmark datasets—NSL KDD and UNSW-NB15—to create and test machine learning models that aim to improve SDN network security and reduce potential threats.