Intrusion detection in cyber-physical system using rsa blockchain technology

Connected cyber and physical elements exchange information through feedback in a cyber-physical system (CPS). Since CPS oversees the infrastructure, it is an integral part of modern living and is viewed as crucial to the development of cutting-edge smart devices. As the number of CPSs rises, so does the need for intrusion detection systems (IDS). The use of metaheuristic methods and Artificial Intelligence for feature selection and classification can offer solutions to some of the problems caused by the curse of dimensionality. In this research, we present a blockchain-based approach to data security in which blocks are generated using the RSA hashing method. Using Differential Evolution (DE), we first select the blockchain-secured data, and then we partition that data into train and testing datasets to use for training and testing our model. It is also permitted for the validated model to use a deep belief network (DBN) to predict attacks. The purpose of the simulation is to evaluate the safety and precision of the classifications. It turns out that the proposed strategy not only improves classification accuracy but also makes the data more resistant to attacks.