A blockchain-based deep learning approach for cyber security in next-generation medical cyber-physical systems

Abstract

Cyber-physical systems (CPSs) have been employed to seamlessly integrate numerous processes and physical components with integrated computing facilities and data storage, aiming to achieve a heightened level of effectiveness and efficiency across various qualitative and quantitative metrics, including technical and organizational aspects. The increased use of the web and the prospering network through IoT (Internet of things) have given a critical open door to CPS to prevail. While this innovation is as of now utilized in programmed pilot flying, advanced mechanics frameworks, clinical checking, modern control frameworks, and so forth, the headway of these frameworks should understand undeniable spotlight on making them proficient and secure. To work on the strength, reliability, and security of these frameworks, specialists can integrate blockchain innovation which has an inbuilt mix of consensual calculations, secure conventions, and circulated information capacity, with the CPS. This introduces an efficient deep learning approach based on blockchain for medical cyber-physical systems (CPS), consisting primarily of two components: a) a blockchain based security framework to protect the medical data and b) the extraction of quintessential features from these data to a classifier for performing the anomaly scans using deep learning. The experimental evaluation demonstrates that the suggested system outperforms existing models, achieving exceptional performance with an accuracy rate of 0.96 and a sensitivity score of 0.95.