Enhancing Security in SDN Environments Through Multi-Layer Authentication and Machine Learning-Based Threat Mitigation

The revolutionary nature of Software-defined networking(SDN) in network management is accompanied by new and more difficult security concerns. This study provides a full security architecture and a robust SDN setup. It combines north and southbound APIs' multi-factor authentication with machine learning-based threat detection. We implement a strict two-factor authentication scheme for the northbound API space using the Ryu SDN framework. SSL/TLS encryption and MongoDB for credentials allow for the authentication of users and applications. This prevents Man-in-the-Middle attacks by verifying user credentials and app IDs in a safe environment. The research uses machine learning to prevent malicious injections when utilizing southbound API. By learning from network traffic data, models like k-nearest neighbours (KNN), Decision Trees(DT), and Random Forests(RF) may distinguish between genuine and malicious packets. By quickly identifying dangerous network behaviours, this method employs malicious packet detection and alert generation. Numerous experiments prove the structure effectively counters security risks. It protects software-defined networks, making sure they stay online and secure. This study contributes to the ongoing conversation about SDN security by providing a comprehensive method for dealing with the complex challenges posed by the rapid evolution of cyber threats in the software-defined networking sector.