https://hdl.handle.net/10259/3847 2026-04-18T22:29:24Z 2026-04-18T22:29:24Z Rodríguez Villagrá, César Martin Reizabal, Sergio Ruiz González, Rubén Basurto Hornillos, Nuño Herrero Cosío, Álvaro https://hdl.handle.net/10259/11497 2026-04-08T11:34:34Z 2026-04-12T00:00:00Z

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 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.

2026-04-12T00:00:00Z Gil Arroyo, Beatriz Cotillas Torres, Clara Herrero Cosío, Álvaro https://hdl.handle.net/10259/11493 2026-04-08T07:38:00Z 2026-03-24T00:00:00Z

Percepción de seguridad en dispositivos IoT domésticos: análisis descriptivo basado en encuesta Gil Arroyo, Beatriz; Cotillas Torres, Clara; Herrero Cosío, Álvaro Informe del proyecto de ciencia ciudadana en el que se recogen y analizan los datos de las encuestas de percepción sobre seguridad de dispositivos IoT domésticos. Esta actividad se lleva a cabo en ejecución del Proyecto Estratégico "Inteligencia Artificial para la Securización de Dispositivos IoT" (C032.23), fruto de un convenio de colaboración suscrito entre el Instituto Nacional de Ciberseguridad (INCIBE) y la Universidad de Burgos. Esta iniciativa se realiza en el marco de los fondos del Plan de Recuperación, Transformación y Resiliencia, financiadas por la Unión Europea (Next Generation), el proyecto del Gobierno de España que traza la hoja de ruta para la modernización de la economía española, la recuperación del crecimiento económico y la creación de empleo, para la reconstrucción económica sólida, inclusiva y resiliente tras la crisis de la COVID19, y para responder a los retos de la próxima década.

2026-03-24T00:00:00Z Redondo Guevara, Raquel Santos González, Pedro Bustillo Iglesias, Andrés Sedano, Javier Villar, José R. Correa, Maritza Alique, José Ramón Corchado, Emilio https://hdl.handle.net/10259/11485 2026-03-21T01:05:42Z 2009-01-01T00:00:00Z

A Soft Computing System to Perform Face Milling Operations Redondo Guevara, Raquel; Santos González, Pedro; Bustillo Iglesias, Andrés; Sedano, Javier; Villar, José R.; Correa, Maritza; Alique, José Ramón; Corchado, Emilio In this paper we present a soft computing system developed to optimize the face milling operation under High Speed conditions in the manufacture of steel components like molds with deep cavities. This applied research presents a multidisciplinary study based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures industrial tools. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough. The second phase is focus on identifying a model for the face milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel tools. Comunicación presentada en: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II

2009-01-01T00:00:00Z Martínez González, Branly Alberto Cambra Baseca, Carlos Urda Muñoz, Daniel Rincón Arango, Jaime Andrés Herrero Cosío, Álvaro https://hdl.handle.net/10259/11273 2026-03-16T09:20:25Z 2026-01-23T00:00:00Z

CyberFlowIoT-GICAP: Labelled Flow-Based Network Traffic Dataset for Cyberattack Detection [Dataset] Martínez González, Branly Alberto; Cambra Baseca, Carlos; Urda Muñoz, Daniel; Rincón Arango, Jaime Andrés; Herrero Cosío, Álvaro This study presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment. The dataset captures network communication generated by hardware-based IoT devices during normal operation, including MQTT messaging, database synchronization, and web-based monitoring, as well as during the execution of predefined cyber-attack scenarios within an isolated experimental network. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAP format. The packet captures were subsequently processed into bidirectional network flows using a flow-based traffic extraction pipeline, producing flow records with statistical and temporal attributes derived from the observed packet exchanges. Cyberattack-related flows were identified based on predefined attack execution time intervals obtained from experimental metadata. Network flows observed outside these intervals were labelled as benign and correspond to regular device communication. The dataset is distributed through a structured repository that includes raw packet captures, processed flow-level datasets in tabular format, and metadata files describing the experimental setup, attack scenarios, and labelling criteria. The data support flow-based analysis of IoT network traffic and the evaluation of cyberattack detection methods.

2026-01-23T00:00:00Z