LATIDIA · Ciberseguridad

LogiC-Diff: Incrustación de propiedades de seguridad en sistemas ciberfísicos habilitados para IA

arXiv:2609.38381v1 Tipo de anuncio: nuevo Resumen: los sistemas ciberfísicos (CPS) habilitados para IA son altamente vulnerables a entradas adversarias y anómalas, donde pequeñas perturbaciones pueden inducir errores en cascada y un control inseguro

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arXiv:2609.38381v1 Announce Type: new Abstract: AI-enabled Cyber-Physical Systems (CPS) are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading errors and unsafe control actions. Existing approaches, such as rule-based filtering, training-time regularization, or diffusion-based reconstruction, either operate outside the model or lack mechanisms to incorporate formal security specifications into the prediction process. In this paper, we take the first step toward embedding security properties directly into AI-enabled CPS, enabling predictive models to enforce system-level constraints during inference rather than relying on external defenses. We introduce a logic-conditioned bi-stage diffusion framework that integrates Signal Temporal Logic (STL) specifications into forecasting. STL serves as a first-class conditioning signal