Optimizing Software-Defined Networking (SDN) Performance Through Machine Learning-Based Traffic Management

This paper proposes a hybrid machine learning-based framework for Software Defined Networking (SDN) environments, integrating a Deep Q-Network (DQN) for intelligent routing optimization and an Autoencoder for anomaly detection. The system dynamically learns optimal routing policies while simultaneously identifying network threats in real-time. Both real and synthetic datasets were used to validate the framework, demonstrating improved network efficiency and detection accuracy. Experimental results confirm the framework’s capability to adapt to diverse traffic patterns, optimize network flow, and secure SDN infrastructures effectively.