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

Muhammad Usman1, Muhammad Shahzad Sarfraz1, Usman Habib2

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.

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Summary
This summary is machine-generated.

This study automates role engineering for secure access control in the Industrial Internet of Things (IIoT) using machine learning. Artificial neural networks and extreme learning machines enhance privacy and user access rights in SCADA systems.

Keywords:
Industrial Internet of Things (IIoT)Internet of Things (IoT)access controldeep learningindustry 4.0privacy preservationresource-constrained IoTrole propagation

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The Industrial Internet of Things (IIoT) integrates connected devices for critical infrastructure automation, generating vast data for decision-making.
  • Supervisory Control and Data Acquisition (SCADA) systems are vital for managing IIoT operations, requiring reliable and secure data exchange.
  • Current access control methods in IIoT rely on manual role engineering, which is inefficient and prone to errors.

Purpose of the Study:

  • To explore the potential of supervised machine learning for automating role engineering in fine-grained access control for IIoT environments.
  • To propose a framework utilizing artificial neural networks (ANN) and extreme learning machines (ELM) for automated role engineering in SCADA-enabled IIoT systems.
  • To compare the effectiveness and performance of ANN and ELM in ensuring data privacy and user access rights.

Main Methods:

  • Developed a mapping framework to apply supervised machine learning algorithms for role engineering.
  • Implemented and fine-tuned a multilayer feedforward artificial neural network (ANN).
  • Implemented and evaluated an extreme learning machine (ELM).

Main Results:

  • Both ANN and ELM demonstrated significant performance in automating role engineering for access control in IIoT.
  • The proposed machine learning approach effectively ensures data privacy and user access rights to resources.
  • Experimental results validated the effectiveness of the proposed scheme for SCADA-enabled IIoT environments.

Conclusions:

  • Supervised machine learning offers a promising solution to automate the tedious process of role engineering in IIoT access control.
  • ANN and ELM are effective algorithms for enhancing security and managing user access in critical IIoT infrastructure.
  • This research paves the way for future advancements in automated role assignment within the IIoT domain.