Dataset of anomalies and malicious acts in a cyber-physical subsystem

Pedro Merino Laso; David Brosset; John Puentes

doi:10.1016/j.dib.2017.07.038

Dataset of anomalies and malicious acts in a cyber-physical subsystem

Data Brief. 2017 Jul 20:14:186-191. doi: 10.1016/j.dib.2017.07.038. eCollection 2017 Oct.

Authors

Pedro Merino Laso¹, David Brosset^{1

2}, John Puentes^{1

3}

Affiliations

¹ Chair of Naval Cyber Defense, École Navale - CC 600, F29240 Brest Cedex 9, France.
² Naval Academy Research Institute, École Navale - CC 600, F29240 Brest Cedex 9, France.
³ Institut Mines-Télécom Atlantique, Lab-STICC CNRS UMR 6285, Equipe DECIDE, F-29238 Brest, France.

Abstract

This article presents a dataset produced to investigate how data and information quality estimations enable to detect aNomalies and malicious acts in cyber-physical systems. Data were acquired making use of a cyber-physical subsystem consisting of liquid containers for fuel or water, along with its automated control and data acquisition infrastructure. Described data consist of temporal series representing five operational scenarios - Normal, aNomalies, breakdown, sabotages, and cyber-attacks - corresponding to 15 different real situations. The dataset is publicly available in the .zip file published with the article, to investigate and compare faulty operation detection and characterization methods for cyber-physical systems.

Keywords: Anomaly; Cyber-physical system; Sensor data; Systems security.