Cited by 224Smart Grid Security and ResilienceNetwork Security and Intrusion DetectionAdversarial Robustness in Machine Learning

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Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

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Kravchik, Moshe, Shabtai, Asaf·2022·IEEE Transactions on Dependable and Secure Computing

DOI 10.1109/tdsc.2021.3050101

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Abstract

Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both the economy and human life. Hence, these systems have become an attractive target for physical and cyber attacks alike. In this article, we examine an attack detection method based on simple and lightweight neural networks, namely, 1D convolutional neural networks and autoencoders. We apply these networks to both the time and frequency domains of the data and discuss the pros and cons of each representation approach. The suggested method is evaluated on three popular public datasets, and detection rates matching or exceeding previously published detection results are achieved, while demonstrating a small footprint, short training and detection times, and generality. We also show the effectiveness of PCA, which, given proper data preprocessing and feature selection, can provide high attack detection rates in many settings. Finally, we study the proposed method’s robustness against adversarial attacks that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect. Our results show that the proposed method is robust to such evasion attacks: in order to evade detection, the attacker is forced to sacrifice the desired physical impact on the system.

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Authors: Kravchik, M., & Shabtai, A.
Affiliations: Ben-Gurion University of the Negev (IL)
ORCID: Kravchik, Moshe
Shabtai, Asaf
Venue: IEEE Transactions on Dependable and Secure Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-07-01
Volume: 19
Issue: 4
Pages: 2179-2197
ISSN: 1545-5971 (ISSN-L)
1941-0018
2160-9209
DOI: 10.1109/tdsc.2021.3050101
OpenAlex: W3119781401
Cited by: 224
References: 56
Citations by year: 2026: 21 · 2025: 55 · 2024: 57 · 2023: 42 · 2022: 25
FWCI: 16.46
Type: journal article
Language: en
Open access: green
zenodo.org
Licence: cc-by
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Dependable and Secure Computing (publishedVersion)
Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders: H2020 European Institute of Innovation and Technology (830927)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords: cyberattack detection, industrial control systems, lightweight neural networks, 1D convolutional neural network, autoencoder, principal component analysis, adversarial attacks, evasion attacks, physical assault, detection robustness

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Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

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