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

• Published in: Journal of parallel and distributed computing Vol. 153; pp. 150 - 160
• Main Authors: Nguyen, Gia Nhu, Viet, Nin Ho Le, Elhoseny, Mohamed, Shankar, K., Gupta, B.B., El-Latif, Ahmed A. Abd
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2021
• Subjects: Blockchain; Cyber–physical system; Deep learning; Intrusion detection; Security; Deep learning; Security; Cyber–physical system; Intrusion detection; Blockchain
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2021.03.011

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. •Secure intrusion detection with blockchain in the healthcare sector is proposed.•It involves a series of cyber–physical system and deep learning processes.•The presented model takes place on NSL-KDD 2015, CIDDS-001, and ISIC dataset.•Presented DBN model has achieved a detection rate of 98.95% and 98.94% on the applied datasets.•Effective classification performance with ResNet 101 model achieving maximum sensitivity.