A Blockchain-Fortified Deep Learning Approach for Intrusion Detection in Healthcare IoT
Puja Archana Das, Chitra Jain, Biswajit Jana, Bireshwar Mazumder, Ansul, Moutushi Singh, Deepsubhra Guha Roy · 2026
The Internet of Things (IoT) has several security issues when applied in any healthcare domain. At the same time, integrating IoT with mainstream healthcare systems has significantly improved the quality of healthcare services. In the Healthcare System (HS), there is a concern regarding the use of wearable devices and sensors. These devices continuously monitor and transmit data, but unfortunately, they often do so through unsecured open channels. This study aims to investigate the integration of artificial intelligence into blockchain-based identity management systems for dynamic access control in IoT-enabled healthcare networks. It explores how AI algorithms, such as machine learning-based risk assessment and adaptive authorization models, can enhance the precision and responsiveness of access control mechanisms in healthcare scenarios. This paper further analyzes the synergies and challenges of integrating AI with blockchain in healthcare systems. It delves into the complexities of leveraging AI to augment the agility and intelligence of decentralized identity and access management within the blockchain framework. This study presents a revolutionary adaptable blockchain architecture that uses the Zero Information Proof (LZIP) technique to guarantee data integrity and the safe exchange of information. Here, we first provide the detection method in healthcare and then the data is added to the blockchain network. The NSL-KDD and ISCX UNSW-NB 2015 datasets were used to test the proposed method, which is more advanced than other existing ones in both blockchain and non-blockchain situations and it produced close to 98% accuracy.