A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security

Abstract

IIoT “Industrial Internet of Things” refers to a subset of Internet of Things technology designed for use in industrial processes and industrial environments. IIoT aims to make manufacturing facilities, energy systems, transportation networks, and other industrial systems smarter, more efficient and connected. The goal of IIoT is to reduce costs, increase productivity, and support more sustainable operations by making industrial processes more efficient. In this context, the use of IIoT is increasingly increasing in manufacturing, energy, healthcare, transportation, and other industries. IoT has become one of the fastest-growing and expanding areas in the history of information technology. Billions of devices communicate with the Internet of Things with almost no human intervention. IIoT consists of sophisticated analysis and processing structures that handle data generated by internet-connected machines. IIoT devices vary from sensors to complex industrial robots. Security measures such as patch management, access control, network monitoring, authentication, service isolation, encryption, unauthorized entry detection, and application security are implemented for IIoT networks and devices. However, these methods inherently contain security vulnerabilities. As deep learning (DL) and machine learning (ML) models have significantly advanced in recent years, they have also begun to be employed in advanced security methods for IoT systems. The primary objective of this systematic survey is to address research questions by discussing the advantages and disadvantages of DL and ML algorithms used in IoT security. The purpose and details of the models, dataset characteristics, performance measures, and approaches they are compared to are covered. In the final section, the shortcomings of the reviewed studies are identified, and open issues in the literature are discussed.