2026-09-07T15:44:50Zhttps://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

Rodríguez García, Rafael Herrero Cosío, Álvaro 2025-11-18T13:35:56Z 2025-11-18T13:35:56Z 2025-11 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 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. eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Mathematical epidemiology Malware propagation Stochastic simulation Data science IoT network Stochastic Simulation Dataset of IoT Malware Spread Using Individual-Based SIR Models and Topological Overlap Measures dataset