Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning

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Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning

Usman, Muhammad (författare): Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan

Sarfraz, Muhammad Shahzad (författare): Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan

Habib, Usman (författare): AI and Data Science Department, FAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, Islamabad Campus, Islamabad 44000, Pakistan

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Aftab, Muhammad Umar (författare): Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan

Javed, Saleha, 1990- (författare): Luleå tekniska universitet,EISLAB

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MDPI, 2023
2023
Engelska.
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:8

Relaterad länk:: https://ltu.diva-por... (primary) (Raw object); visa fler...; https://urn.kb.se/re...; https://doi.org/10.3...; visa färre...

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Abstract Ämnesord

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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.

Ämnesord

TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Industrial Internet of Things (IIoT)
privacy preservation
Resource-Constrained IoT
access control
role propagation
Industry 4.0
Internet of Things (IoT)
deep learning
Cyberfysiska system
Cyber-Physical Systems

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Av författaren/redakt...: Usman, Muhammad; Sarfraz, Muhamma ...; Habib, Usman; Aftab, Muhammad ...; Javed, Saleha, 1 ...

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Kommunikationssy ...

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Datavetenskap

Artiklar i publikationen: Sensors

Av lärosätet: Luleå tekniska universitet

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