Advanced DDoS Attack Detection and Mitigation in Software-Defined Networking (SDN) Environments: An Integrated Machine Learning Approach

Abstract

The increasing sophistication of Distributed Denial of Service (DDoS) attacks poses critical challenges to network security, necessitating advanced detection and mitigation strategies. This research presents a machine learning-based framework that effectively distinguishes between normal and malicious traffic using engineered features such as unique source counts, flow counts, and packet rates. Among the models evaluated, Random Forest demonstrated the highest accuracy at 95.3%, showcasing its effectiveness in identifying diverse attack patterns. The framework incorporates a dynamic mitigation module that adapts in real time to block or redirect malicious traffic while minimizing disruption to legitimate operations. Comprehensive evaluation confirms its scalability and relevance to real-world network environments. Despite its strengths, limitations include reliance on synthetic datasets and computational demands. Future work will address these challenges by integrating real-world traffic data, exploring advanced learning techniques, and enhancing resource efficiency. This study offers a scalable and adaptive solution to evolving DDoS threats.