Zero Trust Machine Learning Architecture for Adaptive Intrusion Prevention and Risk Mitigation in Hybrid Cloud Ecosystems
Abstract
The rapid adoption of hybrid cloud ecosystems has increased enterprise flexibility while simultaneously expanding the attack surface across on-premises infrastructure, public cloud services, virtual machines, containers, applications, identities, APIs, and distributed networks. Conventional perimeter-based security models are increasingly insufficient because users, workloads, and devices frequently operate across multiple trust boundaries. Zero Trust Architecture provides a security paradigm based on continuous verification, least-privilege access, micro-segmentation, and explicit authorization rather than implicit trust. Machine learning can strengthen this model by dynamically analyzing behavioral, contextual, and environmental signals to identify anomalous activity and adapt security controls. This study investigates a Zero Trust machine learning architecture for adaptive intrusion prevention and risk mitigation in hybrid cloud ecosystems. The proposed framework integrates identity behavior, device posture, network activity, workload characteristics, access patterns, threat intelligence, vulnerability information, and resource sensitivity into a continuous risk-assessment mechanism. Machine-learning models are employed for behavioral profiling, anomaly detection, intrusion classification, risk scoring, and adaptive policy optimization. The methodology evaluates detection accuracy, false-positive rates, attack prevention, response latency, risk-score calibration, policy effectiveness, and computational overhead. Experiments also examine concept drift, compromised identities, lateral movement, incomplete telemetry, adversarial manipulation, and dynamic workload migration. The proposed architecture aims to enable continuous and context-aware security decisions that dynamically adjust access privileges and preventive controls while maintaining operational flexibility. The research contributes a practical framework for integrating machine learning with Zero Trust principles to strengthen adaptive intrusion prevention and risk mitigation across complex hybrid cloud environments
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 9 No. 6 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
16742-16756 |
Published |
December 12, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Nancy Al Kalach (%2024). Zero Trust Machine Learning Architecture for Adaptive Intrusion Prevention and Risk Mitigation in Hybrid Cloud Ecosystems. International Journal of Emerging Trends in Engineering and Management Research , Vol. 9 No. 6 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 16742-16756. https://doi.org/10.15662/ijetemr.2024.0906002 |
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