MACHINE LEARNING IN BYOD SECURITY: THREE-LAYERED ACCESS CONTROL FRAMEWORK FOR ENHANCED THREAT DETECTION AND POLICY MANAGEMENT

Abstract

Existing access control provides a security solution to manage BYOD policies but is limited to controlling and providing adequate security. This paper comprehensively implements access control encompassing three security layers of the BYOD policy simultaneously: tactical, strategic, and operational. This system comprises the initial component and dynamic attributes for enforced access decisions. The second component consists of risk monitoring and anomaly detection algorithms. Finally, the third component employs the adaptive policy adjustment algorithm, which provides recommendations to the administration for policy updates in cases of abnormal access based on the results of the attack detection algorithm. The suggested access control solution was implemented
using machine learning algorithms to detect anomalous and atypical user behavior. The experimental results obtained from the UNSW-NB15 dataset confirmed that the proposed access control could improve the anomaly detection algorithm and adaptive policy adjustment performance while reducing prediction detection time. The results demonstrated that the risk monitoring and anomaly detection algorithm, with a prediction time of 0.5 seconds and an accuracy rate of 0.95 percent, is the most effective method for monitoring attacks. Additionally, the results indicated that the accuracy of the adaptive policy adjustment algorithm was approximately 97%, with a threshold value of 0.26 being the optimal modification threshold value. The solution could enhance the detection
of insider threats, access control, and policy management while at the same time making access control dynamic, adaptable, flexible, and secure.