Machine Learning-Enhanced Cross-Tier Security and Anomaly Detection in Wireless Body Area Networks

Abstract

Nowadays, in the dynamic landscape of e-health, the relentless pace of information technology advances brings both transformative possibilities and heightened security concerns, particularly within WBANs. At the forefront of this challenge is the critical imperative to fortify security measures across healthcare Internet of Things environments. This research addresses this pressing issue by championing a pioneering three-layered defense system, thoughtfully integrating the potency of machine learning techniques. The paramount emphasis is on achieving cross-tier security, strategically targeting the device, Hub, and Cloud layers. The groundbreaking SenseGuard anomaly detection system, fueled by sophisticated machine learning algorithms, not only elevates security at the Device layer but strategically reinforces patient wellness and the entire WBAN network. Simultaneously, an innovative intrusion detection algorithm fortifies the Hub layer, while adaptive machine learning models in the Cloud layer ensure a comprehensive defense. This approach provides an effective solution to the security challenges inherent in healthcare IoT as a remarkable achievement in advancing the state-of-the-art, placing cross-tier security at the forefront of WBAN innovation.