Enhancing Real-Time Detection of Distributed Denial of Service (DDoS) Attacks on IoT Infrastructure
Mutaz Khaled Yousef Abdel Wahed, Azmi Halasa, Mowafaq Salem Alzboon · 2025
Distributed Denial of Service (DDoS) attacks pose a significant threat to Internet of Things (IoT) infrastructures, leading to service disruptions and substantial financial losses. This research proposes an advanced real-time DDoS detection framework by integrating feature selection techniques with metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA), alongside machine learning ML classifiers, including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). The feature selection process identifies the most informative network traffic attributes, optimizing model training and improving detection performance. Experimental results demonstrate that the proposed approach enhances detection accuracy, reduces false positives, and achieves low detection time, ensuring rapid threat mitigation. This makes it a robust and scalable solution for securing IoT environments against evolving DDoS threats.