Ayedh, M. Aljuaid Turkea and Wahab, Ainuddin Wahid Abdul and Idris, Mohd Yamani Idna (2024) Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment. CMC-Computers Materials & Continua, 80 (3). ISSN 1546-2218, DOI https://doi.org/10.32604/cmc.2024.055287.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya Impact Oriented Interdisciplinary Research Grant (IIRG008-19IISS) |
| Uncontrolled Keywords: | BYOD; security; access control; access control decision-enforcement; deep learning; neural network techniques; TabularDNN; multilayer; dynamic; adaptable; flexibility; bottlenecks performance; policy conflict |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Jan 2025 01:02 |
| Last Modified: | 06 Jan 2025 01:02 |
| URI: | http://eprints.um.edu.my/id/eprint/47073 |
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