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.

网络物理系统（CPS）是物理过程与处理和数据传输的结合。由于患者医疗数据的法律和道德观点，网络安全是医疗保健中的一个主要且具有挑战性的问题。因此，用于医疗保健应用的CPS模型的设计需要特别注意以确保数据安全。为了解决这个问题，本文提出了一种基于医疗保健领域CPS分类模型的基于区块链的数据传输安全入侵检测方法。提出的模型使用传感器设备执行数据采集过程，入侵检测使用深度置信网络（DBN）模型进行。此外，提出的模型使用多份创建（MSC）模型来生成捕获图像的多份，从而实现隐私和安全。此外，区块链技术被用于将数据安全传输到云服务器，云服务器执行基于残差网络（ResNet）的分类模型来识别疾病的存在。使用NSL-KDD 2015，CIDDS-001和ISIC数据集对提出的模型进行实验验证。仿真结果指出了所提出模型的有效结果。