Software Defined Networks (SDN) enhance network programmability by separating the control and data planes, yet challenges remain in performance, traffic optimization, and security. This paper evaluates the integration of machine learning (ML) techniques, including K-Nearest Neighbor, Decision Tree, Support Vector Machine, Bayesian models, and Deep Neural Networks, to improve SDN performance. Experiments across multiple scenarios demonstrate that ML algorithms can enhance traffic prediction, detect anomalies, and mitigate DDoS attacks, achieving up to 100% accuracy in specific configurations. The study highlights the potential of ML to significantly improve SDN efficiency, security, and scalability.

Software Defined Network (SDN) Machine Learning (ML) Outliers Supervised Learning Unsupervised Learning Reinforcement Learning Distributed Denial of Service (DDoS) Anomalies Detection Traffic Engineering Elephant Flow Convolutional Neural Network (CNN) Deep Learning (DL) Support Vector Machine (SVM) Decision Tree Naïve Bayes Port Scanning Attacks