International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Driven Zero-Trust Threat Detection for Cloud Virtual Machines

Author(s) Mr. Dileep BS, Mr. Darshan Ramesh, Mr. Ganesh G, Ms. Brunda P
Country India
Abstract Cloud computing’s exponential growth has dramatically transformed organizational approaches to infrastructure deployment, management, and protection. Virtual Machines represent the foundation of Infrastructure-as-a-Service platforms, offering organizations unprecedented flexibility, resource optimization, and financial benefits. Nevertheless, this technological evolution has created novel security vulnerabilities requiring innovative solutions. We present a novel AI-powered Zero-Trust security framework specifically engineered for cloud-based Virtual Machine environments, combining Zero-Trust architectural principles with intelligent behavioral analysis capabilities. Our approach utilizes compact monitoring agents installed within individual VMs to track essential performance indicators continuously. Contrasting with conventional intrusion prevention mechanisms that depend on predetermined rule configurations, our agents transmit behavioral data streams to a central processing unit where advanced machine learning algorithms identify malicious patterns. Comprehensive testing utilizing the CICDDoS2019 benchmark dataset
validates that our framework achieves ninety-nine percent detection precision using Random Forest and Decision Tree classification methods, demonstrating substantial improvements over conventional rule-dependent security systems.
Keywords Zero-Trust Architecture, Cloud Security, Virtual Machine Introspection, Machine Learning, Anomaly Detection, Random Forest, SVM, LSTM, CICDDoS2019, IaaS Security, Behavioral Monitoring.
Field Computer > Network / Security
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-21
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.64045
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E-ISSN 2582-2160
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