Explainable Security Risk Analytics for Embedded Networks and Cloud Infrastructure

Abstract

Modern digital infrastructure faces increasingly complex security threats across embedded devices, cloud platforms, and large-scale network environments. This article proposes an explainable security risk analytics framework that integrates network anomaly detection, vulnerability quantification, and cloud infrastructure security assessment. The framework combines ensemble learning for traffic anomaly detection, CVSS-based vulnerability metrics for risk scoring, and policy-driven evidence collection for cloud environments. SHAP-based explanation is used to identify the most influential traffic and vulnerability features behind model outputs. The article argues that effective cybersecurity analytics must move beyond isolated detection models toward integrated, interpretable, and policy-aligned risk assessment. By combining embedded network monitoring with cloud vulnerability analysis, the proposed framework supports more transparent prioritization of remediation actions. The study contributes to cybersecurity research by linking explainable machine learning, vulnerability scoring, and cloud security governance into a unified risk analytics model.