Blockchain and Machine Learning for Security Attack Detection in Industrial IoT Networks

Internet of Things IoT networks are becoming essential channels for transmitting very sensitive data among the most important corporations in the world when it comes to industrial infrastructure. In response to the inherent hazards posed by these kinds of communication channels, creative solutions using machine learning and blockchain algorithms have separately emerged. Our goal in this research has been to effectively combine these many approaches into an all-encompassing security architecture for Internet of Things (IoT) device networks. With this integrated approach, any risks will be recognised, secure information transmission protocols will be initiated, and the computational capabilities of industrial IoT systems will be complied with. These goals are well met by the suggested solution, which positions itself as a useful tool for identifying and containing intrusions in an Internet of Things network. Notably, in certain situations, it outperforms traditional intrusion detection systems (IDS). Through the integration of Blockchain and deep learning techniques, this study provides a strong basis for enhancing the security of business internet of things networks, providing a competitive advantage over current risk reduction approaches.