Title: Weighted chi-squared and LightGBM-based bagging for enhanced intrusion detection in edge IoMT networks

Authors: Abdelkarim Ait Temghart; Hmad Zennou; Mbarek Marwan; Mohamed Baslam

Addresses: TIAD Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal, Morocco ' TIAD Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal, Morocco ' ENSIAS, Mohammed V University in Rabat, Madinat Al Irfane, Rabat, 713, Morocco ' TIAD Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal, Morocco

Abstract: With the rapid growth of internet of medical things (IoMT) devices, edge computing providers must proactively address cybersecurity threats. This study leverages artificial intelligence (AI) for detecting malicious activities by utilising the synthetic minority oversampling technique (SMOTE) to balance class distributions in the imbalanced dataset. It employs a weighted hybrid feature selection method combining chi-squared and light gradient boosting machine (LightGBM). The proposed solution introduces a new ensemble model using bagging with multiple base classifiers like random forest, extra trees, XGBoost, and others. Using benchmark simulated data from the Washington University St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS), the results demonstrate that the bagging of XGBoost achieves a high accuracy of 99.04%, showcasing its effectiveness in detecting cyber threats compared to other baseline models.

Keywords: edge computing; security; ensemble learning; feature selection; machine learning; bagging; network attacks; intrusion detection system; IDS; internet of medical things; IoMT.

DOI: 10.1504/IJICS.2026.150537

International Journal of Information and Computer Security, 2026 Vol.29 No.1, pp.20 - 43

Received: 14 Nov 2024
Accepted: 14 Jun 2025
Published online: 16 Dec 2025 *

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