Comparative Analysis of ML and DL Approaches for Securing IoMT Environments

The Internet of Medical Things (IoMT) leverages interconnected devices to enhance healthcare operations through data collection, monitoring, and automation. However, IoMT systems are vulnerable to cyberattacks, necessitating robust detection mechanisms. This paper presents a comprehensive comparison of traditional Machine Learning (ML) and Deep Learning (DL) models for detecting attacks in IoMT environments. We address class imbalance using the Synthetic Minority Over-sampling Technique and evaluate models based on various classification performance metrics and computational complexity. Our findings highlight that XGBoost (XGB) achieves the highest performance metrics, including accuracy (0.957), F1-score (0.955), and AUC (0.992), while maintaining competitive prediction times. In comparison, DL models, despite their high accuracy, exhibit significantly higher computational demands. This study demonstrates the effectiveness of XGB for real-time attack detection in IoMT systems, offering practical insights for selecting models that balance performance with computational efficiency. This research contributes to the development of more resilient IoMT systems by enhancing cyberattack detection strategies.