ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
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A systematic literature review categorizing 32 papers on threat and attack modelling for CPS and noting that current models fail to address dynamic, multi-layer, multi-path, and multi-agent attack characteristics.
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From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems
ASTRAL applies multimodal LLMs with prompt chaining and few-shot learning to synthesize CPS architectures from disparate sources, enabling adaptive threat identification and quantitative risk estimation, as supported by ablation studies and feedback from 14 cybersecurity practitioners.
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Security Modelling for Cyber-Physical Systems: A Systematic Literature Review
A systematic literature review categorizing 32 papers on threat and attack modelling for CPS and noting that current models fail to address dynamic, multi-layer, multi-path, and multi-agent attack characteristics.