SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems

• URL: http://arxiv.org/abs/2604.23905v1
• Date: Sun, 26 Apr 2026 22:40:11 GMT
• Title: SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems
• Abstract summary: SMSI (System Model Security Inference) is a hybrid neuro-symbolic pipeline that produces a prioritized list of NIST 800-53 security controls.<n>The prototype has three main stages: a deterministic mapping system components to vulnerabilities via the NVD; a family of retrieval and classification models linking vulnerabilities to MITRE ATT&CK techniques; and a control recommender.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Threat modeling for cyber-physical systems (CPS) remains a largely manual exercise. This project presents SMSI (System Model Security Inference), a hybrid neuro-symbolic pipeline that starts from a SysML architecture model and produces a prioritized list of NIST 800-53 security controls. The prototype has three main stages: a deterministic parser mapping system components to vulnerabilities via the NVD; a family of retrieval and classification models linking vulnerabilities to MITRE ATT&CK techniques; and a control recommender. We explore three approaches for CVE-to-ATT&CK mapping: a supervised classifier using fine-tuned SecureBERT+, retrieval-based dense encoders, and a zero-shot LLM approach using Gemma-4 26B. We validate the pipeline on a healthcare IoT gateway with nine software components. For the ATT&CK-to-NIST stage, pretrained SecureBERT achieves the highest control retrieval scores, demonstrating that dense embeddings provide a strong basis for automated control recommendation.

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