A Novel lightGBM-bayesian approach for DDoS detection in SDN environments

Abstract

Software-Defined Networks (SDN) have revolutionized network management by introducing a centralized controller. However, this centralization renders SDN vulnerable to Distributed Denial of Service (DDoS) attacks, posing critical security challenges. While existing studies explore attack vulnerabilities in SDN, they often suffer from limitations related to memory management and efficient detection. To address these issues, we propose a novel model that leverages the Light-Gradient Boost Machine (LGBM) algorithm, coupled with Bayesian Optimization for hyperparameter tuning. Our model achieves an exceptional average accuracy of 99.18% on the UNSW-15 dataset during both training and testing phases, surpassing existing models in terms of accuracy and training time. By outperforming current solutions, our proposed DDoS attack detection model significantly enhances SDN security, providing a robust defense mechanism against emerging threats.