Publication: Madics: A methodology for anomaly detection in industrial control systems
Authors
Perales Gómez, Ángel Luis ; Fernández Maimó, Lorenzo ; García Clemente, Félix J. ; Huertas Celdrán, Alberto
item.page.secondaryauthor
item.page.director
Publisher
MDPI
publication.page.editor
publication.page.department
DOI
https://doi.org/10.3390/sym12101583
item.page.type
info:eu-repo/semantics/article
Description
© 2020. The authors. This document is made available under the CC-BY-SA 4.0 license http://creativecommons.org/licenses/by-sa /4.0/
This document is the published version of a published work that appeared in final form in Symmetry.
To access the final work, see DOI: https://doi.org/10.3390/sym12101583
Abstract
Industrial Control Systems (ICSs) are widely used in critical infrastructures to support the essential services of society. Therefore, their protection against terrorist activities, natural disasters, and cyber threats is critical. Diverse cyber attack detection systems have been proposed over the years, in which each proposal has applied different steps and methods. However, there is a significant gap in the literature regarding methodologies to detect cyber attacks in ICS scenarios. The lack of such methodologies prevents researchers from being able to accurately compare proposals and results. In this work, we present a Methodology for Anomaly Detection in Industrial Control Systems (MADICS) to detect cyber attacks in ICS scenarios, which is intended to provide a guideline for future works in the field. MADICS is based on a semi-supervised anomaly detection paradigm and makes use of deep learning algorithms to model ICS behaviors. It consists of five main steps, focused on pre-processing the dataset to be used with the machine learning and deep learning algorithms; performing feature filtering to remove those features that do not meet the requirements; feature extraction processes to obtain higher order features; selecting, fine-tuning, and training the most appropriate model; and validating the model performance. In order to validate MADICS, we used the popular Secure Water Treatment (SWaT) dataset, which was collected from a fully operational water treatment plant. The experiments demonstrate that, using MADICS, we can achieve a state-of-the-art precision of 0.984 (as well as a recall of 0.750 and F1-score of 0.851), which is above the average of other works, proving that the proposed methodology is suitable for use in real ICS scenarios.
publication.page.subject
Citation
Symmetry 2020, 12(10), 1583
item.page.embargo
Collections
Ir a Estadísticas
Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by-sa/4.0/