A Blockchain-Based, Deep-Learning Strategy for Safe Healthcare Data Transfer Across the Internet of Things

Due to IoT’s ability to provide remote access and constant monitoring of patient data, conventional healthcare systems have been transformed into intelligent ones. Wearables and other Internet of Things (IoT)-based medical gadgets are used to monitor patients’ vital signs in real time, such as temperature and blood sugar levels, among other things. The Internet of Things has the potential to improve remote medical diagnosis and care. Integrating IoT devices with conventional hospital infrastructure has enhanced service quality. However, the sensors and wearable technology in a healthcare system are constantly monitoring vital signs and sending that information to other devices or computers in the area across an unprotected open channel. Although the increased efficiency gained by IoT devices communicating with servers is welcome, the increased vulnerability to cyber assaults that might endanger patients under close watch is not. In this piece, we develop a Deep Learning strategy depending on the Blockchain for safe data transmission in IoT-enabled healthcare systems. The proposed new architecture makes use of the proposed security mechanism to guarantee data integrity and safe data transfer. The IoT Healthcare Security Dataset was downloaded from Kaggle first. Then, Tag splash error normalisation was used to impute the data. Consencus'striolayer block chain architecture was used to determine the authorised user from the normalised data. Once the user data has been validated, it may undergo feature extraction using the Elite profund component analysis to detect assaults. For a reliable IDS, the deep hidden bias attention neural network (DHBANN) was developed. Experiments using public data show that the suggested method achieves 99.5% accuracy in attack prediction, outperforming state-of-the-art methods in both blockchain and non-blockchain situations.