SecureRiskNet: An Advanced AI-Driven Framework for Intelligent Security Risk Detection in Heterogeneous Cloud-Fog Computing Networks

The cloud-fog computing paradigm has transformed the field of distributed computing and at the same time brought about unparalleled security risks to the critical infrastructure systems. Conventional security models are limited in real-time threat identification and this leads to high false positive vulnerabilities, as well as poor response mechanisms. This paper presents a new AI-supported security architecture, called SecureRiskNet, which is a synergistic system of advanced deep learning architectural frameworks and smart risk quantification algorithms that are used to detect all threats in heterogeneous cloud-fog networks. Our approach methodology uses hybrid detection mechanism as a combination of Isolation Forest to detect anomalies without supervision, and Long Short-Term Memory (LSTM) networks to identify temporal patterns, detecting both known and zero-day attacks. The framework views the multi-layered feature fusion techniques as the correlation of network traffic patterns to cloud resource utilization metrics and generates better cross-domain anomaly detection. SecureRiskNet achieves 96.2% accuracy, 0.95 precision, 0.96 recall, and 0.967 AUC, while maintaining sub-1.5 ms inference latency across fog deployments, demonstrating superior performance and real-time suitability for resource-constrained environments. The offered entropy-weighted dynamic risk scoring with the Principal Component Analysis (PCA) optimization facilitates prioritized threat management in real-time and allows the deployment on distributed infrastructures.