RAGIIoT: Risk-aware attack graph generation for IIoT via automated CVE-tactic mapping

The complexity of Industrial IoT (IIoT) environments introduces cyber risks, especially where legacy and modern systems converge. This paper focuses on a lightweight dynamic threats analysis framework by converting static attack graphs into dynamic probabilistic attack graphs (PAGs). Utilising real-time anomalies, CVSS vulnerabilities, and MITRE ATT\&CK\textsuperscript{\textregistered} mappings, it quantifies threat propagation via probabilistic inference. Edges are dynamically weighted by tactic relevance, exploit severity, and device criticality, adapting to evolving attack chains. QoS-aware risk prioritization balances mitigation urgency, asset availability, and performance, optimizing resource allocation. This enables proactive defense in resource-constrained IIoT environments while ensuring operational continuity.