LATIDIA · Ciberseguridad

Jev-IDS: Modelos System One para la detección de intrusiones en la red

arXiv: 2610.01079v1Tipo de anuncio: nuevo Resumen: Los sistemas de detección de intrusiones en la red (IDS) de aprendizaje automático dependen de conjuntos de datos etiquetados sustanciales y capacitación específica para tareas, mientras que la detección de modelos de lenguaje grande (LLM)

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arXiv:2610.01079v1 Announce Type: new Abstract: Machine-learning Network Intrusion Detection Systems (IDS) depend on substantial labeled datasets and task-specific training, whereas Large Language Models (LLMs) detection can analyze flow records directly but incurs higher inference cost and latency, with less constrained outputs. This paper presents JEV-IDS, an open experimental general NIDS based on the Jev System One Model (SOM) to detect zero day intrusions Under label scarcity. JEV-IDS serializes one flow per request and asks JEV two questions: a binary attack probability and a finite-choice traffic category. Our results show that, at k=1, JEV was 4.8 times faster and 3.8 times cheaper than GPT-5.6 Luna, with 1.5 times higher novel-attack recall;