LATIDIA · Ciberseguridad

Un marco de viabilidad consciente de la implementación para la detección de intrusiones de IoT basada en el aprendizaje automático en arquitecturas de borde, niebla y nube

arXiv: 2610.08867v1Tipo de anuncio: nuevo Resumen: El rápido crecimiento y la heterogeneidad de los entornos de Internet de las cosas (IoT) han expuesto las limitaciones fundamentales en la detección de intrusiones tradicional basada en reglas y firmas

La noticia

arXiv:2610.08867v1 Announce Type: new Abstract: The rapid growth and heterogeneity of Internet of Things (IoT) environments have exposed fundamental limitations in traditional rule-based and signature-based intrusion detection systems. This paper presents a quantitative deployment-aware analysis of machine learning (ML)-based intrusion detection approaches across edge, fog/gateway, and cloud architectures. Unlike prior surveys that primarily emphasize detection accuracy, this work defines representative quantitative deployment capability envelopes extracted from experimental and system-level studies and introduces a structured Deployment Feasibility Score (DFS) model. The proposed framework maps ML techniques to architectural layers based on computational demand, memory footprint, and latency sensitivity using a weighted ordinal scoring mechanism. The analysis demonstrates that lightweight statistical and