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

The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.

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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Research works printed but not linked: 5

  1. 10.1007/978–3-319-71368-7_8 · DOI · Paper page
  2. Morris, Industrial control system simulation and data logging for intrusion detection system research, 3
  3. Goodfellow, 2016, Deep Learning, 1
  4. Croarkin, 2006, NIST/SEMATECH e-handbook of statistical methods, Nist/Sematech
  5. 10.1007/BF00113892 · DOI · Paper page

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Metadata

Authors
Kravchik, M., & Shabtai, A.
Affiliations
Ben-Gurion University of the Negev (IL)
ORCID
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
Licence
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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