Citation

Sen, Anik and Heng, Swee Huay and Tan, Shing Chiang (2026) Enhancing DDoS Attack Detection Through Hybrid Ensemble Machine Learning Technique and Explainable AI. In: 9th International Conference on Innovation in Artificial Intelligence, ICIAI 2025, 13 March 2025 - 15 March 2025, Hybrid, Singapore.

Text Enhancing DDoS Attack Detection Through Hybrid Ensemble Machine Learning Technique and Explainable AI _ Springer Nature Link.pdf - Published Version Restricted to Repository staff only Download (669kB)

Abstract

In the continuously growing field of cybersecurity, Distributed Denial of Service (DDoS) attack detection is a continuous challenge. This study presents a hybrid machine learning framework that enhances detection accuracy and interpretability by integrating multiple machine learning models with explainable AI techniques. In our proposed model, Gradient Boosting and Extreme Gradient Boosting (XGBoost) shows excellent performance and SHapley Additive exPlanations (SHAP) to identify key features enabling a transparent understanding of the factors contributing to attack identification. The dataset undergoes meticulous preprocess-ing, including missing value dealing, encoding for categorical values, and numerical feature normalisation. The proposed Hybrid Ensemble model (GB+XGB) demonstrates strong performance, achieving a perfect precision score of 100% and accuracy of 99.96%. The comprehensive analysis demonstrates the efficacy of hybrid models in achieving high detection accuracy and transparency through explainable AI using SHAP analysis. This research significantly contributes to providing a robust, interpretable framework for DDoS attack detection, potentially informing future developments in threat detection systems and enhancing the security infrastructure.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	DDoS attack detection, Explainable AI
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Oct 2026 04:40
Last Modified:	30 Oct 2026 04:40
URII:	http://shdl.mmu.edu.my/id/eprint/17042

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