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AI-Powered Cloud Misconfiguration Detection Using Machine Learning
Cloud infrastructures have become foundational to modern enterprise operations, enabling scalable deployment of services across geographically distributed environments. Despite this widespread adoption, cloud security remains a persistent concern, largely driven by configuration errors that expose sensitive resources to unauthorized access. Conventional Cloud Security Posture Management (CSPM) tools rely on static, rule-driven mechanisms that struggle to adapt to emerging threat patterns in complex multi-cloud deployments. This paper presents a machine learning–based framework for automated detection and classification of cloud misconfigurations. The proposed system employs a Random Forest classifier trained on structured historical configuration records sourced from Amazon Web Services, Microsoft Azure, and Google Cloud Platform. A multi-stage pipeline encompasses data ingestion, categorical feature encoding, temporal feature extraction, model-based prediction, and risk score computation. The trained model is served through a Flask REST API and paired with an interactive web dashboard that provides security analysts with real-time visibility into misconfiguration categories and associated severity levels. Experimental evaluation yields an overall classification accuracy of 94%, a precision of 92%, a recall of 90%, and an F1-score of 91%, confirming the viability of ensemble learning for automated cloud vulnerability management. The modular architecture supports future integration of anomaly detection and Infrastructure-as-Code analysis
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