2026-04-19T15:45:45Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/105082025-05-30T00:05:11Zcom_10259_2604col_10259_5684
Original and processed dataset of malware propagation in IoT networks with a SIR epidemiological model
Sainz Villegas, Leticia
Casado Vara, Roberto Carlos
Basurto Hornillos, Nuño
Cambra Baseca, Carlos
Urda Muñoz, Daniel
Herrero Cosío, Álvaro
Mathematical epidemiology
Graph theory
Malware propagation
Data science
IoT network
Informática
Inteligencia artificial
Computer science
Artificial intelligence
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.
2025-05-29T11:52:40Z
2025-05-29T11:52:40Z
2025-04-21
dataset
info:eu-repo/semantics/acceptedVersion
http://hdl.handle.net/10259/10508
10.71486/r4de-dj18
eng
info:eu-repo/grantAgreement/INCIBE//AI4SECIoT/ES/Artificial Intelligence for Securing IoT Devices/
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
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north=42.35; west=-3.706667; name=Burgos, Spain
start=2025-04; end=2025-04
Universidad de Burgos