LATIDIA · Ciberseguridad

Sistema de detección forense de intrusiones en la red con inteligencia artificial explicable y cadena de custodia digital

arXiv: 2611.06173v1Tipo de anuncio: nuevo Resumen: Implementar un modelo de detección de intrusos explicable desde un entorno de laboratorio validado en un flujo de trabajo forense requiere garantías de que los puntos de referencia no prueben: que el ce

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arXiv:2611.06173v1 Announce Type: new Abstract: Deploying an explainable intrusion detection model from a validated laboratory setting into a forensic workflow requires guarantees that benchmarks do not test: that the certified model is the exact artifact applied to a case, that heterogeneous evidence can be classified reliably, and that every step is verifiable. We present MODEXRE, a forensic pipeline that extends a previously validated synthetic-data-trained XGBoost classifier with SHAP explainability (arXiv:2607.00763) into a dual-mode architecture: a Laboratory mode, where models are trained and certified on public and synthetic data, and a Formal mode, where a frozen, hash-verified model performs inference on case evidence under a SHA-256 hash-chained chain of custody. Suricata