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Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning
The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146338/ https://www.ncbi.nlm.nih.gov/pubmed/37112271 http://dx.doi.org/10.3390/s23083931 |
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author | Usman, Muhammad Sarfraz, Muhammad Shahzad Habib, Usman Aftab, Muhammad Umar Javed, Saleha |
author_facet | Usman, Muhammad Sarfraz, Muhammad Shahzad Habib, Usman Aftab, Muhammad Umar Javed, Saleha |
author_sort | Usman, Muhammad |
collection | PubMed |
description | The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain. |
format | Online Article Text |
id | pubmed-10146338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101463382023-04-29 Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning Usman, Muhammad Sarfraz, Muhammad Shahzad Habib, Usman Aftab, Muhammad Umar Javed, Saleha Sensors (Basel) Article The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain. MDPI 2023-04-12 /pmc/articles/PMC10146338/ /pubmed/37112271 http://dx.doi.org/10.3390/s23083931 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Usman, Muhammad Sarfraz, Muhammad Shahzad Habib, Usman Aftab, Muhammad Umar Javed, Saleha Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning |
title | Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning |
title_full | Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning |
title_fullStr | Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning |
title_full_unstemmed | Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning |
title_short | Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning |
title_sort | automatic hybrid access control in scada-enabled iiot networks using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146338/ https://www.ncbi.nlm.nih.gov/pubmed/37112271 http://dx.doi.org/10.3390/s23083931 |
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