2026-04-19T15:46:06Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/110782025-11-20T09:08:16Zcom_10259_3847com_10259_5086com_10259_2604col_10259_8557col_10259_5684

Stochastic Simulation Dataset of IoT Malware Spread Using Individual-Based SIR Models and Topological Overlap Measures Rodríguez García, Rafael Herrero Cosío, Álvaro Mathematical epidemiology Malware propagation Stochastic simulation Data science IoT network Seguridad informática Redes informáticas Computer security Computer networks This dataset contains simulation data generated from two individual-based stochastic models for malware propagation in an Internet-of-Things (IoT) network: a continuous-time Gillespie SIR model and a discrete-time Monte Carlo SIR model. For each modeling framework, two variants are included: the standard version (Gil / LMC) and the version incorporating the Topological Overlap Measure (TOM) (GilT / LMCTOM). All simulations are executed on a 128-node IoT communication network generated as a power-law cluster graph. Each simulation is stored as an independent CSV file in pivoted format, where rows represent network nodes and columns represent temporal steps produced by the algorithm (event steps in the Gillespie method and iteration steps in the Monte Carlo method). The dataset is suitable for research on malware propagation, stochastic processes on networks, graph-based machine learning models, and cybersecurity analytics. 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. 2025-11-18T13:35:56Z 2025-11-18T13:35:56Z 2025-11 dataset info:eu-repo/semantics/acceptedVersion Rodríguez García, R., & Herrero, A. (2025). Stochastic Simulation Dataset of IoT Malware Spread Using Individual-Based SIR Models and Topological Overlap Measures [Data set]. Universidad de Burgos. https://doi.org/10.71486/K6V4-0Z45 https://hdl.handle.net/10259/11078 10.71486/k6v4-0z45 eng info:eu-repo/grantAgreement/INCIBE//MR5Ñ05/ES/Inteligencia Artificial para la Securización de Dispositivos IoT/AI4SECIoT/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/zip text/csv text/plain north=42.351; west=-3.689; name=Escuela Politécnica Superior (Universidad de Burgos), Burgos, Spain start=2025-11; end=2025-11 Universidad de Burgos