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

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.