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Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Aljuaid Turkea Ayedh M1,2,*, Ainuddin Wahid Abdul Wahab1,*, Mohd Yamani Idna Idris1,3

1 Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
2 Faculty of Computer Science and Information Technology, Shaqra University, Shaqra, 11961, Saudi Arabia
3 Center for Mobile Cloud Computing, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia

* Corresponding Authors: Aljuaid Turkea Ayedh M. Email: email; Ainuddin Wahid Abdul Wahab. Email: email

Computers, Materials & Continua 2024, 80(3), 4663-4686. https://doi.org/10.32604/cmc.2024.055287

Abstract

Organizations are adopting the Bring Your Own Device (BYOD) concept to enhance productivity and reduce expenses. However, this trend introduces security challenges, such as unauthorized access. Traditional access control systems, such as Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC), are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources. This paper proposes a method for enforcing access decisions that is adaptable and dynamic, based on multilayer hybrid deep learning techniques, particularly the Tabular Deep Neural Network TabularDNN method. This technique transforms all input attributes in an access request into a binary classification (allow or deny) using multiple layers, ensuring accurate and efficient access decision-making. The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94% accuracy rate. Additionally, the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point (PAP). This solution significantly improves the flexibility of access control systems, making them more dynamic and adaptable to the evolving needs of modern organizations. Furthermore, it offers a scalable approach to manage the complexities associated with the BYOD environment, providing a robust framework for secure and efficient access management.

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Cite This Article

APA Style
M, A.T.A., Wahab, A.W.A., Idris, M.Y.I. (2024). Adaptable and dynamic access control decision-enforcement approach based on multilayer hybrid deep learning techniques in BYOD environment. Computers, Materials & Continua, 80(3), 4663-4686. https://doi.org/10.32604/cmc.2024.055287
Vancouver Style
M ATA, Wahab AWA, Idris MYI. Adaptable and dynamic access control decision-enforcement approach based on multilayer hybrid deep learning techniques in BYOD environment. Comput Mater Contin. 2024;80(3):4663-4686 https://doi.org/10.32604/cmc.2024.055287
IEEE Style
A.T.A. M, A.W.A. Wahab, and M.Y.I. Idris, “Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment,” Comput. Mater. Contin., vol. 80, no. 3, pp. 4663-4686, 2024. https://doi.org/10.32604/cmc.2024.055287



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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