Enhanced DDoS Detection Using Feature Selection and Multi-Model Machine Learning Frameworks
K. Muthamil Sudar, P. Nagaraj, V. Vaissnave · Advances in computational intelligence and robotics book series · 2025
Distributed Denial of Service (DDoS) attacks are among the most pervasive and disruptive threats in today's interconnected digital landscape, capable of overwhelming systems and rendering services unavailable. The effective and timely detection of DDoS attacks has therefore become critical for maintaining the security and availability of networked systems. This chapter presents a holistic approach to DDoS detection based on machine learning techniques, improved by feature selection and the use of multiple predictive models The system uses a diverse ensemble of machine learning models: Random Forest, Support Vector Machine, Gradient Boosting, and Neural Networks to effectively learn the complex patterns typical of DDoS traffic This chapter highlights the potential of integrating feature selection with multi-model machine learning frameworks for efficient and scalable DDoS detection.The proposed framework will be able to counter the evolving nature of DDoS attacks, hence offering resilience and reliability in ever-increasingly complex network environments.