A Robust Ensemble Learning for DDoS Attack Classification on the Internet of Medical Things
Year: 2026
Type: article
Abstract: Distributed Denial of Service (DDoS) attacks pose significant threats to the Internet of Medical Things (IoMT), potentially disrupting critical healthcare services. A significant challenge in detecting these attacks is the high imbalance in network traffic data, which can bias classification models. This study introduces a hybrid approach that integrates the XGBoost algorithm with Principal Component Analysis (PCA) and undersampling to address class imbalance and enhance detection performance. Three configurations were tested: (1) XGBoost + PCA + Undersampling, (2) XGBoost + PCA, and (3) XGBoost + Undersampling. The model was evaluated using a publicly available multiclass DDoS dataset under an 80:20 training-testing split. The XGBoost + Undersampling method achieved the highest performance, with accuracy, precision, recall, and F1-score of 99.98%. Despite these results, potential limitations—such as data loss due to undersampling and excluding cross-validation or external testing. Thus, the proposed ensemble learning technique has proven to robustly improve the performance of DDoS attack detection in unbalanced dataset conditions
Institution: Telkom University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7135213866