LogiC-Diff: Embedding Security Properties Into AI-Enabled Cyber-Physical Systems

The paper introduces LogiC-Diff, a logic-conditioned bi-stage diffusion framework that embeds Signal Temporal Logic (STL) specifications into AI-enabled cyber‑physical system (CPS) forecasting models. By using STL as a conditioning signal, the method repairs inputs and refines outputs to jointly mitigate adversarial perturbations and enforce desired temporal behaviors. Experiments on two real‑world CPS datasets show that LogiC-Diff consistently improves robustness and specification compliance across various sensor faults and cyber attacks, outperforming reconstruction‑based defenses.

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• rag
• diffusion
• benchmarks
• safety