Advanced Threat Detection in Zero Trust Architectures: A Machine Learning Approach

• Author(s): Job Adegede
• Paper ID: 1709633
• Page: 1456-1469
• Published Date: 01-08-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 1 July-2025

Abstract

This study investigates the integration of machine learning algorithms within zero trust security frameworks to enhance threat detection capabilities. Using a dataset of 1.2 million network events collected from three enterprise environments, we evaluate six supervised and unsupervised learning techniques for identifying anomalous behavior patterns that indicate potential security breaches. The research specifically focuses on optimizing the balance between minimizing false positives and maintaining detection sensitivity. Our findings demonstrate that ensemble models combining deep learning with traditional detection methods achieve up to 96.7% accuracy while reducing false positives by 73.4% compared to conventional rule-based systems. This research provides empirical evidence supporting the efficacy of machine learning-augmented zero trust architectures for advanced threat detection in modern enterprise environments.

Keywords

Machine Learning, Zero Trust, Threat Detection, Anomaly Detection, Ensemble Models, Enterprise Security

Citations

IRE Journals:
Job Adegede "Advanced Threat Detection in Zero Trust Architectures: A Machine Learning Approach" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 1456-1469

IEEE:
Job Adegede "Advanced Threat Detection in Zero Trust Architectures: A Machine Learning Approach" Iconic Research And Engineering Journals, 9(1)