Mechanical System Vulnerabilities: Advanced Fusion Machine Learning Model for Detecting Ethereum Attack Patterns in IIoT-5.0 Environments

It is of the utmost significance to keep the reliability and confidentiality of these systems in check in light of the coming advent of Industry 5.0 as well as the growing penetration of the Industrial Internet of Things (IIoT) into complicated mechanical systems. The broad adoption of blockchain technology, and in particular Ethereum, brings with it a new set of opportunities as well as hazards that have an impact on the complicated dynamic that exists between machines, data, and decentralized applications. This research investigates an advanced fusion machine learning model with the intention of locating dangerous patterns and likely attack vectors that target Ethereum transactions and contracts within IIoT-5.0 systems. The results of this investigation are intended to be used in future research. The detection of these patterns and vectors is the primary focus of the model. In an era in which there is a transition taking place in the industrial sector, this research presents a novel technique for improving cybersecurity. Examining the specific flaws that may exist in mechanical systems after they have been integrated with blockchain solutions is the strategy that will be utilized. Our model shows tremendous promise in recognizing abnormalities and potential dangers by utilizing a combination of supervised learning techniques and unsupervised learning approaches. These techniques allow for the model to develop without being directly monitored by a human. This paves the way for preventative safety precautions to be included in the design of the future generation of industrial systems.