2026-05-23T17:55:49Zhttps://riubu.ubu.es/oai/request

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

2026-05-21T11:50:20Z urn:hdl: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 Event-based logging Digital twin Ground truth labeling Discrete-event systems PLC scan cycles Industrial cybersecurity Fault injection Sensor and actuator faults 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. 2026-05-21T11:50:20Z 2026-05-21T11:50:20Z 2026-05-19 dataset https://hdl.handle.net/10259/11686 10.71486/2drf-1g53 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Universidad de Burgos