A Two-Level Machine Learning Based-Intrusion Detection System for IoT Healthcare Application Based on Blockchain

Abstract

: Smart healthcare systems offer human-centric solutions that enable the remote monitoring of patients, particularly those who are elderly, disabled, or located in geographically remote regions, thereby enhancing the quality and accessibility of medical services. These systems leverage core technologies such as the Internet of Medical Things (IoMT), blockchain, and artificial intelligence to facilitate the analysis and secure sharing of medical data among various stakeholders in the healthcare ecosystem. However, the transmission of sensitive health information over public networks raises significant security and privacy concerns. To address these issues, we propose a role-based access control protocol that restricts unauthorized access to the Ethereum blockchain and enforces rules for data usage. In addition, cryptographic primitives are employed to ensure data confidentiality. Our security framework also incorporates a two-level machine learning-based Intrusion Detection System (IDS): the first operates at the IoT gateway level to monitor IoT devices traffic, while the second is integrated within the blockchain network to detect and prevent malicious Ethereum transactions. Experimental evaluation on the Edge-IIoT and Ethereum fraud datasets demonstrates that the proposed IDS achieves high effectiveness across key metrics as accuracy, precision, recall, and F1-score. Random Forest outperforms all other algorithms, with accuracy rates of 97.12% for inside IDS and 98.2% for outside IDS. A comparison with state-of-the-art solutions demonstrates that our approach outperforms existing methods in both detection accuracy and security. Security analysis further confirms the system’s robustness against diverse cyberattacks.