ENHANCING DATA SECURITY IN HEALTHCARE IOT SYSTEMS USING EMERGING TECHNOLOGIES

Abstract

Healthcare Internet of Things (H-IoT) technologies has transformed patient monitoring, diagnosis and healthcare management. But with this constant transfer of sensitive medical data between interdependent devices comes a lot of security and privacy issues, such as unauthorized access, data breaches, malware attacks, and insider threats. In traditional security settings, the dynamic and limited resources of IoT environments are inadequately catered for by conventional security solutions. To improve data confidentiality, integrity, and availability in healthcare IoT systems, this paper introduces an integrated security framework that integrates the Blockchain technology with the federated learning (FL) approach and the artificial intelligence (AI)-based intrusion detection system (IDS). Blockchain ensures that medical records are not tampered with, and homomorphic encryption allows for secure computation over the encrypted healthcare data. In federated learning, patients' privacy is maintained by training the model in a decentralized manner without sharing their raw data. In addition, an AI-based intrusion detection system detects malicious activity in real-time. The experimental results indicate that the proposed framework can provide the attack detection accuracy of 98.7%, data integrity assurance of 99.3%, and reduce the security overhead by 35% compared with the conventional approach. The results show that the adoption of emerging technologies can greatly enhance the security of healthcare IoT systems without compromising their efficiency or regulatory compliance.