Contenido principal del artículo

Diego Narciandi-Rodríguez
Universidad de León
España
https://orcid.org/0009-0006-3241-2546
Jose Aveleira-Mata
Universidad de León
España
https://orcid.org/0000-0001-5439-0997
Alicia Merayo Corcoba
Universidad de León
España
https://orcid.org/0000-0001-6219-1808
Manuel Rubiños
Universidade da Coruña
España
https://orcid.org/0009-0001-4085-0451
Paula Arcano-Bea
Universidade da Coruña
España
https://orcid.org/0009-0004-6706-5519
Héctor Alaiz-Moretón
Universidad de León
España
https://orcid.org/0000-0001-6572-1261
Núm. 45 (2024), Computadores y Control
DOI: https://doi.org/10.17979/ja-cea.2024.45.10804
Recibido: may. 29, 2024 Aceptado: jul. 8, 2024 Publicado: jul. 15, 2024
Derechos de autor

Resumen

Desde hace unos años la aparición y uso de dispositivos IoT (Internet de las Cosas), los cuales destacan por el uso de protocolos ligeros debido a su baja carga computacional, hace que surgan nuevos vectores de ataque en en los sistemas con dispositivos IoT. Es por ello que es necesario entrenar y desarrollar modelos de aprendizaje automático a partir de datos reales, que se implementen en sistemas de deteccion de intrusiones (IDS). Aquí es donde intervienen los datasets los cuales posibilitan esta actividad gracias al desarrollo efectivo de estos modelos. En este trabajo se presenta el desarrollo de un disector de tramas que facilita la generación datasets específicos para los diferentes protocolos IoT existentes que sean útiles para crear modelos de aprendizaje automático a partir de los mismos.

Detalles del artículo

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