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

作　　者：Aljuaid Turkea Ayedh M Ainuddin Wahid Abdul Wahab Mohd Yamani Idna Idris 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第9期4663-4686,共24页计算机、材料和连续体（英文）

基　　金：partly supported by the University of Malaya Impact Oriented Interdisci-plinary Research Grant under Grant IIRG008(A,B,C)-19IISS.

摘　　要：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 Tabular DNN 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 themmore dynamic and adaptable to the evolving needs ofmodern 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.

关 键 词：BYOD security access control access control decision-enforcement deep learning neural network techniques TabularDNN MULTILAYER dynamic adaptable FLEXIBILITY bottlenecks performance policy conflict 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]