2026-07-20T10:18:39Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/105082025-05-30T00:05:11Zcom_10259_2604col_10259_5684

Sainz Villegas, Leticia Casado Vara, Roberto Carlos Basurto Hornillos, Nuño Cambra Baseca, Carlos Urda Muñoz, Daniel Herrero Cosío, Álvaro north=42.35; west=-3.706667; name=Burgos, Spain start=2025-04; end=2025-04 2025-05-29T11:52:40Z 2025-05-29T11:52:40Z 2025-04-21 http://hdl.handle.net/10259/10508 10.71486/r4de-dj18 The dataset contains the data generated by an individual SIR model in an IoT network simulated by a graph for 20 time steps. This dataset is designed for training graph-based AI models for malware propagation detection in IoT networks. This publication is part of the AI4SECIoT project ("Artificial Intelligence for Securing IoT Devices"), funded by the National Cybersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade. image/png text/csv text/plain eng Universidad de Burgos Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Mathematical epidemiology Graph theory Malware propagation Data science IoT network Informática Inteligencia artificial Computer science Artificial intelligence Original and processed dataset of malware propagation in IoT networks with a SIR epidemiological model dataset info:eu-repo/semantics/acceptedVersion info:eu-repo/grantAgreement/INCIBE//AI4SECIoT/ES/Artificial Intelligence for Securing IoT Devices/ 2025