https://hdl.handle.net/10259/5684 Sat, 23 May 2026 17:10:06 GMT 2026-05-23T17:10:06Z https://hdl.handle.net/10259/11686 DAYPSCI v1: An Event-Based Dataset of Fault Injection Scenarios in PLC-Controlled Industrial Cyber-Physical Systems Martín Fraile, Juan Vicente; Basurto Hornillos, Nuño; Sierra García, Jesús Enrique; Herrero Cosío, Álvaro The dataset contains time-series data collected from an industrial cyber-physical system (CPS) based on a PLC-controlled part marking station using Siemens S7-1200 and S7-1500 devices. Data acquisition follows an event-based logging approach, where changes in system variables are recorded together with their associated duration (Δt), enabling precise temporal characterization while reducing redundancy. In addition to temporal information, the dataset explicitly represents scan-level execution by incorporating identifiers of PLC scan cycles (scan_id) and the relative order of events within each scan (event_order). This allows accurate representation of multiple events occurring within the same control cycle and preserves the logical execution order of the system. The dataset includes both normal operation and fault conditions generated through controlled fault injection, specifically targeting sensors and actuators (e.g., solenoid valves). Ground truth labels are derived from the experimental configuration provided to the control system and embedded during data acquisition, ensuring consistency between system behavior and annotation. Data are organized into independent experimental batches, each corresponding to a specific operating condition. Each batch includes processed event-based data (CSV), raw network traffic captures in PCAPNG format, including industrial PROFINET communication traffic, and supporting documentation, enabling traceability and reproducibility. The dataset is designed to support the development, training, and evaluation of machine learning models for anomaly detection, fault classification, and industrial cybersecurity applications, while also enabling detailed temporal and logical analysis of discrete-event industrial processes. Tue, 19 May 2026 00:00:00 GMT https://hdl.handle.net/10259/11686 2026-05-19T00:00:00Z https://hdl.handle.net/10259/11498 Images_dataset_NF-UNSW-NB15-v3_1d_to_2d Villar Val, Álvaro; Martínez Fuentes, Virginia; Granados López, Diego; Arroyo Puente, Ángel; Herrero Cosío, Álvaro This dataset represents a higher-dimensional extension of the NF-UNSW-NB15 v3 dataset [Luay et al., 2025; Luay et al., 2025], in which the correlations among variables are explicitly considered and used to organize them spatially as pixels. Sun, 22 Mar 2026 00:00:00 GMT https://hdl.handle.net/10259/11498 2026-03-22T00:00:00Z https://hdl.handle.net/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 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. Sun, 12 Apr 2026 00:00:00 GMT https://hdl.handle.net/10259/11497 2026-04-12T00:00:00Z https://hdl.handle.net/10259/11496 Iterative Stepped Search algorithm for the clique partitioning problem Solana Ezquerra, Mario; Pacheco Bonrostro, Joaquín; Casado Yusta, Silvia The dataset is a set of txt files with the fictitious instaces used in the paper "Iterative Stepped Search algorithm for the clique partitioning problem" The dataset is a text file containing the solutions obtained in the article “Iterative Stepped Search Algorithm for the Clique Partitioning Problem” for a set of instances from the DIMACS library. Sat, 21 Mar 2026 00:00:00 GMT https://hdl.handle.net/10259/11496 2026-03-21T00:00:00Z