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Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model.

Hany F Atlam1,2, Muhammad Ajmal Azad1, Nawfal F Fadhel3

  • 1School of Computing and Engineering, University of Derby, Derby DE22 1GB, UK.

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|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Neuro-Fuzzy System (NFS) for dynamic risk estimation in Internet of Things (IoT) access control. The NFS model offers timely and accurate security risk assessments for adaptive IoT security.

Keywords:
Internet of ThingsNFS modelrisk estimationrisk-based access controlsecurity risk

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Existing access control models for IoT applications rely on static policies, failing to adapt to dynamic and unpredictable situations.
  • Dynamic access control models, such as risk-based approaches, require accurate real-time risk estimation, which is challenging without historical data.
  • The need for adaptive and context-aware security in the Internet of Things (IoT) is critical due to the proliferation of connected devices and evolving threat landscapes.

Purpose of the Study:

  • To propose a novel Neuro-Fuzzy System (NFS) model for estimating security risk values in real-time for IoT access control.
  • To address the research gap in dynamic, reliable, and accurate risk estimation techniques for access control systems lacking predefined datasets.
  • To evaluate the performance and efficiency of the proposed NFS model in providing contextual-aware access decisions.

Main Methods:

  • Development of a Neuro-Fuzzy System (NFS) model to dynamically estimate security risk based on real-time features.
  • Training the NFS model using three distinct machine learning algorithms: Levenberg-Marquardt (LM), Conjugate Gradient with Fletcher-Reeves (CGF), and Scaled Conjugate Gradient (SCG).
  • Evaluation of the NFS model's risk estimation accuracy, processing time, and applicability in simulated IoT access control scenarios, including a children's hospital setting.

Main Results:

  • The Levenberg-Marquardt (LM) algorithm proved to be the optimal choice for training the NFS model, ensuring efficient risk estimation.
  • The proposed NFS model demonstrated a short and efficient processing time, suitable for timeliness in various IoT applications.
  • The NFS model successfully provided dynamic and contextual-aware access decisions when evaluated against real-world access control scenarios.

Conclusions:

  • The Neuro-Fuzzy System (NFS) offers a viable and effective solution for dynamic security risk estimation in IoT environments.
  • The LM-trained NFS model provides a computationally efficient and accurate method for real-time risk assessment, enhancing IoT security.
  • The proposed model's ability to adapt to changing conditions makes it a valuable tool for implementing context-aware access control in diverse IoT applications.