Industrial healthcare systems aim to provide progressive real-time monitoring of patients and improve medical services through data sharing among intelligent wearable sensors and devices. However, this connectivity carries fundamental vulnerabilities associated with privacy and security because of the requirement of constant communication and monitoring over a public network. However, new technologies, namely Deep Learning (DL) and Blockchain (BC), are promising to overcome these problems and modernize healthcare systems. BC technology is receiving important attention because of its permanent and decentralized nature. Incorporating the immutable and decentralized nature of BC technology with Intrusion Detection Systems (IDS) can provide a stronger and more trustworthy security structure for IoT healthcare systems. This study presents a Secure Internet of Things Framework Using Blockchain and Advanced Feature Selection (SIoTF-BCAFS) model for smart healthcare. The aim is to improve security and reliability in IoT-enabled smart healthcare systems using advanced techniques. Initially, BC-assisted data transmission is employed to ensure secure and transparent communication between devices, especially in IoT. Then, min-max scaling is applied in the data preprocessing step to convert the input data. The ReliefF technique is used to select the best features. Additionally, the SIoTF-BCAFS technique implements an Autoencoder (AE) along with a Temporal Convolutional Network (TCN) model to effectively capture sequential patterns and temporal dependencies. The experimental analysis of the SIoTF-BCAFS technique was performed on the ToN-IoT dataset, demonstrating superior accuracy over existing approaches.
Ahmed S. Alfakeeh (Mon,) studied this question.