Enhancing RMF and ATT&CK Mapping Accuracy Through Integration of Sentence-BERT and Mitigation Parameters

To minimize cybersecurity risks in weapon systems, the implementation of the Korean Risk Management Framework (K-RMF) has become imperative. However, a significant “strategic gap” exists between high-level RMF controls and technical MITRE ATT&CK techniques, rendering manual mapping labor-intensive. This study proposes an automated mitigation-driven pipeline that integrates Sentence-BERT (SBERT) with the structural defense relationships of the ATT&CK knowledge graph. To address the data coverage limitations of the Center for Threat-Informed Defense (CTID) silver standard, we introduce Recall@restricted as a calibrated performance metric. Experimental evaluations demonstrate that the proposed ensemble framework achieves a Recall@restricted of 0.74, significantly outperforming baseline SBERT-only models. These findings suggest that deterministic mitigation relationships effectively complement semantic representations, providing a robust framework for aligning RMF controls with adversarial behaviors.