Enhancing Access Control, Authorization, and Accountability in Cyber-Physical Systems Using Machine Learning

Tools

Adom, Isaac, Awais, Muhammad, Raza, Mohsin, Khan, Umar and Chughtai, Omer (2024) Enhancing Access Control, Authorization, and Accountability in Cyber-Physical Systems Using Machine Learning. In: 2024 International Conference on Frontiers of Information Technology, FIT 2024. 2024 International Conference on Frontiers of Information Technology, FIT 2024 . The Institute of Electrical and Electronics Engineers (IEEE), PAK. ISBN 9798331510503

Full text not available from this repository. (Request a copy)

Abstract

Cyber-Physical Systems (CPS) integrate networking, computing, and physical processes, forming the backbone of critical industries such as healthcare, energy, and transportation. The increasing complexity and interconnection of CPS have led to significant compliance and security challenges. This research introduces a novel framework that leverages Machine Learning (ML) techniques to enhance access control, authorization, and accountability within CPS environments. By combining these techniques with traditional access control methods, the framework addresses the unique demands of CPS, including scalability and adaptability to dynamic conditions. A key innovation lies in applying ensemble methods like Random Forest, AdaBoost, and Gradient Boosting, which outperform individual models by mitigating overfitting and improving generalizability. The framework also incorporates sophisticated feature engineering and regularization strategies tailored to CPS, ensuring robust and efficient security solutions. Through rigorous data preprocessing, relationship analysis, and model validation, this study demonstrates how machine learning can significantly advance the security posture of CPS, offering a scalable and effective approach tailored to the specific needs of these critical systems.

Item Type: Book Section
Additional Information: Publisher Copyright: © 2024 IEEE.
Uncontrolled Keywords: access control,accountability,authorization,cyber physical systems,data preprocessing,ensemble methods,machine learning,security,artificial intelligence,computer networks and communications,computer science applications,computer vision and pattern recognition,information systems,information systems and management,safety, risk, reliability and quality ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Data Science and AI
Faculty of Science > Research Groups > Health Computing
Faculty of Science > Research Groups > Cyber Intelligence and Networks
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 17 Mar 2026 15:30
Last Modified: 22 Mar 2026 07:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/102385
DOI: 10.1109/FIT63703.2024.10838448

Actions (login required)

View Item View Item