LATIDIA · Ciberseguridad

AutoDP-LLM: Automatización del preprocesamiento de datos para sistemas de detección de intrusiones utilizando modelos de lenguaje grandes

arXiv:2610.05369v1 Tipo de anuncio: nuevo Resumen: La creciente complejidad y escala de los ciberataques modernos exigen sistemas de detección de intrusiones (IDS) inteligentes y computacionalmente eficientes. Sin embargo, el diseño eficaz

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arXiv:2610.05369v1 Announce Type: new Abstract: The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems (IDS). However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative configurations. For large, high-dimensional network traffic data, this process can create a significant computational burden. In this work, we propose AutoDP-LLM, an automated pre-processing framework designed to reduce manual pipeline development and computational overhead. Specifically, AutoDP-LLM leverages Large Language Models (LLMs) to autonomously generate and validate executable data pre-processing pipelines. The framework combines deterministic host-side planning with LLM-based specialist agents to formulate data-processing strategies, synthesize executable code, and adaptively determine retained feature