Efficient IIoT framework for mitigating Ethereum attacks in industrial applications using supervised learning with quantum classifiers

Abstract

Industrial Internet of Things (IIoT) solutions have transformed industrial productivity and operations. The incorporation of Ethereum blockchain technology into IIoT creates new weaknesses, exposing industrial systems to several cyberattacks. An unique IIoT framework mitigates Ethereum-based attacks in industrial applications to solve these vulnerabilities. This system uses supervised learning and quantum classifiers to detect and fix fraudulent Ethereum transaction patterns in real time. Our methodology has lower false positive rates and higher detection accuracy than conventional methods, according to first trials. This study shows that quantum computing and machine learning (ML) can improve the security of Ethereum-enabled IIoT devices in industry.