Improving DDos Attack Identification and Mitigation In SDN Through The Use of Ensemble Online Machine Learning Framework

This paper introduces a comprehensive framework for detecting and mitigating Distributed Denial of Service (DDoS) attacks in Software Defined Networks (SDN) using ensemble Online Machine Learning (OML) techniques. By integrating real-time traffic monitoring, dynamic learning, and adaptive security mechanisms, this model addresses the limitations of traditional detection systems. Experimental results demonstrate its efficacy in enhancing network resilience, achieving significant accuracy and reliability across various attack scenarios. The framework’s modularity and scalability further underline its potential for broader adoption in modern networking environments. Additionally, the paper highlights the challenges and opportunities in integrating such frameworks into existing SDN ecosystems.