Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model

Abstract

Cyber–physical system (CPS) is the incorporation of physical processes with processing and data transmission. Cybersecurity is a primary and challenging issue in healthcare due to the legal and ethical perspective of the patient’s medical data. Therefore, the design of CPS model for healthcare applications requires special attention for ensuring data security. To resolve this issue, this paper proposes a secure intrusion, detection with blockchain based data transmission with classification model for CPS in healthcare sector. The presented model performs data acquisition process using sensor devices and intrusion detection takes place using deep belief network (DBN) model. In addition, the presented model uses a multiple share creation (MSC) model for the generation of multiple shares of the captured image, and thereby achieves privacy and security. Besides, the blockchain technology is applied for secure data transmission to the cloud server, which executes the residual network (ResNet) based classification model to identify the presence of the disease. The experimental validation of the presented model takes place using NSL-KDD 2015, CIDDS-001 and ISIC dataset. The simulation outcome pointed out the effective outcome of the presented model.