2026-04-18T20:35:00Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/114972026-04-08T11:34:34Zcom_10259_3847com_10259_5086com_10259_2604col_10259_8557col_10259_5684

2026-04-08T10:13:40Z urn:hdl:10259/11497 Labeled IoT Window-Based Random Network Pattern Dataset for Reinforcement Learning Rodríguez Villagrá, César Martin Reizabal, Sergio Ruiz González, Rubén Basurto Hornillos, Nuño Herrero Cosío, Álvaro Internet of things Cybersecurity Attack Traffic Benign traffic Network DDoS DoS Reinforcement learning This dataset is designed to support the training and evaluation of reinforcement learning models in the context of network traffic analysis. It is derived from an existing IoT network traffic dataset, from which packet capture (pcap) files were selected and processed following a custom methodology explained in [Methodological Information](methodological-information). The resulting data representation is based on a windowing approach, where network traffic is segmented into fixed-size temporal windows. Each window aggregates traffic instances and is labeled according to its composition as benign, attack, or mixed (containing both benign and malicious activity). The final datasets are generated through random combinations of these windows, enabling the creation of diverse traffic patterns that better reflect dynamic and random network conditions. This structure facilitates the use of the dataset in reinforcement learning scenarios, where agents must learn to identify, classify, or respond to varying traffic behaviors over time. Additionally, the evaluation datasets are generated following the same methodology as the training datasets, but are kept separate and are not used during the training process, allowing for an independent evaluation of model performance. 2026-04-08T10:13:40Z 2026-04-08T10:13:40Z 2026-04-12 dataset https://hdl.handle.net/10259/11497 10.71486/pzvm-3z31 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Universidad de Burgos