RT dataset
T1 DAYPSCI v1: An Event-Based Dataset of Fault Injection Scenarios in PLC-Controlled Industrial Cyber-Physical Systems
A1 Martín Fraile, Juan Vicente
A1 Basurto Hornillos, Nuño
A1 Sierra García, Jesús Enrique
A1 Herrero Cosío, Álvaro
K1 Event-based logging
K1 Digital twin
K1 Ground truth labeling
K1 Discrete-event systems
K1 PLC scan cycles
K1 Industrial cybersecurity
K1 Fault injection
K1 Sensor and actuator faults
AB 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.
PB Universidad de Burgos
YR 2026
FD 2026-05-19
LK https://hdl.handle.net/10259/11686
UL https://hdl.handle.net/10259/11686
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 23-may-2026