A Hybrid Blockchain-based Approach for Multi-Network Intrusion Detection

Nowadays, we advocate employing blockchains with RSA hashing to protect sensitive information. Differential Evolution (DE) selects data stored in a block chain and splits it into several sets to be used for model training and testing. The verified model can foresee intrusions with the help of a Deep Belief Network (DBN). Data from the blockchain is split into "train" and "testing" sets, and the Money-grubbing Simulated Annealing selects these sets for the purposes of training and testing models. Based on the verified model, a deep learning classifier can foretell the occurrence of assaults. Accuracy and security of classifications are tested using simulations. This paper work integrates a blockchain-based approach for data security and intrusion detection combined with Deep Belief Networks (DBNs) and RSA hashing. The findings demonstrate that the suggested method improves both classification precision and data longevity. Experimental results and simulations applied to our strategy better highlight its advantages in terms of performance, efficiency and security.