A cloud-based hybrid intrusion detection framework using XGBoost and ADASYN-Augmented random forest for IoMT

Abstract

Internet of Medical Things have vastly increased the potential for remote patient monitoring, data-driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud-edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost-based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine-tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW-NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy.