Machine Learning Enabled Zero Trust Cybersecurity Architecture for Intelligent Adaptive Hybrid Cloud Protection
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805040Keywords:
Machine Learning, Zero Trust Architecture, Cybersecurity, Hybrid Cloud, Adaptive Security, Threat Detection, Behavioral Analytics, Cloud Security, Micro-Segmentation, Risk-Based Access Control, Intrusion Detection, Intelligent SecurityAbstract
The rapid adoption of hybrid cloud environments has transformed organizational computing by combining private infrastructure, public cloud platforms, edge devices, and distributed applications. However, this complexity has expanded the cyberattack surface and weakened traditional perimeter-based security models. Zero Trust Architecture (ZTA) provides a security paradigm in which no user, device, application, workload, or network connection is inherently trusted, while machine learning (ML) can enhance Zero Trust by continuously analyzing behavioral, contextual, and operational data. This study proposes a machine-learning-enabled Zero Trust cybersecurity architecture for intelligent and adaptive protection of hybrid cloud environments. The proposed architecture integrates identity and access management, continuous authentication, micro-segmentation, policy-based access control, behavioral analytics, threat intelligence, security monitoring, and automated response. ML models analyze user behavior, device posture, network traffic, workload activity, and contextual risk to calculate dynamic risk scores and adapt access policies accordingly. The research methodology combines architectural design, dataset preparation, feature engineering, supervised and unsupervised learning, model evaluation, simulation, and comparative analysis. Performance is evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, latency, and resource overhead. The proposed approach aims to improve attack detection, minimize unauthorized access, and provide scalable, adaptive security across heterogeneous hybrid cloud infrastructures
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