2026-05-23T17:55:37Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/116862026-05-21T12:06:41Zcom_10259_2604col_10259_5684

Martín Fraile, Juan Vicente 341 600 0000-0002-7212-7380 Basurto Hornillos, Nuño 742 600 0000-0001-7289-4689 Sierra García, Jesús Enrique 737 600 0000-0001-6088-9954 Herrero Cosío, Álvaro 283 600 0000-0002-2444-5384 2026-05-21T11:50:20Z 2026-05-21T11:50:20Z 2026-05-19 https://hdl.handle.net/10259/11686 10.71486/2drf-1g53 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. The funding for this work was provided by AI4SECIoT project ("Artificial Intelligence for Securing IoT Devices" - C032.23), funded by the National Cibersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade. eng Universidad de Burgos Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Event-based logging Digital twin Ground truth labeling Discrete-event systems PLC scan cycles Industrial cybersecurity Fault injection Sensor and actuator faults DAYPSCI v1: An Event-Based Dataset of Fault Injection Scenarios in PLC-Controlled Industrial Cyber-Physical Systems dataset info:eu-repo/semantics/acceptedVersion 2026 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/11686/4/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 4 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/11686/3/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 3 ORIGINAL READMEv1.txt READMEv1.txt text/plain 18424 https://riubu.ubu.es/bitstream/10259/11686/1/READMEv1.txt b316360946508478dd8199a7bd77b70f MD5 1 10259/11686 oai:riubu.ubu.es:10259/11686 2026-05-21 14:06:41.548 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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