Publication Type
Journal Article
Publication Date (Issue Year)
2024
Journal Name
Electronics
Abstract
his 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.
Keywords
classical learning algorithm, quantum mechanism, industrial Internet of Things, IIoTsec, quantum classical learning, multifaceted connectivity, architectural design
Rsif Scholar Name
Ismaeel Abiodun Sikiru
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
Université d'Abomey-Calavi, Benin
Funding Statement
This research was funded by Partnership for Skills in Applied Sciences, Engineering and Technology—Regional Scholarship and Innovation Fund (PASET-RSIF). This work was supported in part by the National Science and Technology Council in Taiwan under contract no: NSTC 113-2410-H-030-077-MY2.
Recommended Citation
Sikiru, I. A., Kora, A. D., Ezin, E. C., Imoize, A. L., & Li, C. (2024). Hybridization of learning techniques and quantum Mechanism for IIoT Security: Applications, challenges, and prospects. Electronics, 13 (1), 4153. https://doi.org/10.3390/electronics13214153