https://hdl.handle.net/10259/8557 2026-04-18T22:29:20Z 2026-04-18T22:29:20Z 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 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 Rodríguez García, Rafael Herrero Cosío, Álvaro https://hdl.handle.net/10259/11078 2025-11-20T09:08:16Z 2025-11-01T00:00:00Z

Stochastic Simulation Dataset of IoT Malware Spread Using Individual-Based SIR Models and Topological Overlap Measures Rodríguez García, Rafael; Herrero Cosío, Álvaro 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.

2025-11-01T00:00:00Z Cambra Baseca, Carlos Nascimento, Antonia Maiara Marques do Rad Moradillo, Juan Carlos Ruiz González, Rubén Barros García, Rocío Herrero Cosío, Álvaro https://hdl.handle.net/10259/10909 2025-10-01T07:45:28Z 2025-09-27T00:00:00Z

Red-Edge (725nm) Monochrome Imaging of Vine Leaves Treated with Antifungal Products Cambra Baseca, Carlos; Nascimento, Antonia Maiara Marques do; Rad Moradillo, Juan Carlos; Ruiz González, Rubén; Barros García, Rocío; Herrero Cosío, Álvaro The dataset contains all raw data of the work "Red-Edge (725nm) Monochrome Imaging of Vine Leaves Treated with Antifungal Products"

2025-09-27T00:00:00Z