Hybridization of Learning Techniques and Quantum Mechanism for IIoT Security: Applications, Challenges, and Prospects

Abstract

This article describes our point of view regarding the security capabilities of classical learning algorithms (CLAs) and quantum mechanisms (QM) in the industrial Internet of Things (IIoT) ecosystem. The heterogeneity of the IIoT ecosystem and the inevitability of the security paradigm necessitate a systematic review of the contributions of the research community toward IIoT security (IIoTsec). Thus, we obtained relevant contributions from five digital repositories between the period of 2015 and 2024 inclusively, in line with the established systematic literature review procedure. In the main part, we analyze a variety of security loopholes in the IIoT and categorize them into two categories—architectural design and multifaceted connectivity. Then, we discuss security-deploying technologies, CLAs, blockchain, and QM, owing to their contributions to IIoTsec and the security challenges of the main loopholes. We also describe how quantum-inclined attacks are computationally challenging to CLAs, for which QM is very promising. In addition, we present available IIoT-centric datasets and encourage researchers in the IIoT niche to validate the models using the industrial-featured datasets for better accuracy, prediction, and decision-making. In addition, we show how hybrid quantum-classical learning could leverage optimal IIoTsec when deployed. We conclude with the possible limitations, challenges, and prospects of the deployment.