(untitled) (Eurasian Journal of Engineering and Technology, 2026)
Distributed Denial of Service (DDoS) attacks, which pose a significant threat to network security, have been increasing in both scale and complexity over time. This paper proposes a machine learning–based approach optimized using SHAP (SHapley Additive exPlanations) to address the problem of real-time DDoS attack detection. To the best of our knowledge, this study is among the first to apply SHAP-based feature selection consistently across three international benchmark datasets—CIC-DDoS2019, UNSWNB15, and Network Intrusion (CIC-IDS2017)—and to conduct a comprehensive comparison using four algorithms: Random Forest, K-Nearest Neighbors (KNN), XGBoost, and LightGBM