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MADICS: A Methodology for Anomaly Detection in Industrial Control Systems
- Source :
- Symmetry, Vol 12, Iss 1583, p 1583 (2020), Symmetry; Volume 12; Issue 10; Pages: 1583
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
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.
- Subjects :
- Physics and Astronomy (miscellaneous)
Computer science
General Mathematics
Feature extraction
02 engineering and technology
Machine learning
computer.software_genre
Field (computer science)
0202 electrical engineering, electronic engineering, information engineering
Computer Science (miscellaneous)
critical infrastructures
business.industry
Deep learning
industrial control systems
lcsh:Mathematics
deep learning
020206 networking & telecommunications
anomaly detection
artificial intelligence
industry applications
machine learning
Industrial control system
lcsh:QA1-939
Work (electrical)
Chemistry (miscellaneous)
Feature (computer vision)
Cyber-attack
020201 artificial intelligence & image processing
Anomaly detection
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20738994
- Volume :
- 12
- Issue :
- 1583
- Database :
- OpenAIRE
- Journal :
- Symmetry
- Accession number :
- edsair.doi.dedup.....d71c22a4270fe44cf58869e4a6a0c356