SECURING IOT DEVICES IN HEALTHCARE: CHALLENGES AND SOLUTIONS

Abstract

This review article investigates the use of blockchain technology and powerful machine learning algorithms to improve IoT device security in healthcare. The main objective is to tackle crucial security issues like anomaly detection, authentication, and data integrity. A decentralized, unchangeable ledger for transactions and data transfers is made possible by blockchain technology, guaranteeing strong data integrity and transparency. By detecting abnormalities from typical behavior patterns, advanced machine learning models such as anomaly detection algorithms are used to detect and mitigate security threats in real-time. Furthermore, context-aware dynamic Bayesian networks and blockchain greatly enhance authentication methods, guaranteeing that only authorized users have access to IoT devices and data. The review emphasizes how important it is to strike a balance between strict adherence to regulations and thorough security measures in IoT systems for healthcare. Best practices for defending IoT networks against changing threats are also presented. The results highlight how blockchain and machine learning can be used to secure Internet of Things applications in the healthcare industry, laying the groundwork for more research in this important area.