Cited by 4Network Security and Intrusion DetectionWater Systems and OptimizationSmart Grid Security and Resilience

Detecting cyberattacks based on deep neural network approaches in industrial control systems

Ayas, Selen, Ayas, Mustafa Sinasi, Cavdar, Bora, Sahin, Ali Kivanc·2025·Journal of Information Security and Applications

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Crossref · 10.1016/j.jisa.2025.104206

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46 of 46 references verified0 citation errors · 0 not checkable

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  • Some sensor-correlation-based models learn temporal correlations and cross-sensor dependencies implicitly using deep RNNs, LSTMs, attention mechanisms, or Transformers applied to multivariate sequences.

    “Some models learn temporal correlations and cross-sensor dependencies implicitly using deep RNNs, LSTMs, attention mechanisms, or Transformers applied to multivariate sequences [88,90,93,98,108,110], while others make correlations explicit through graph structures, digital twins, energy-based physics features, or multi-instance representations [94,96,99,101,104,106,107].”
  • The dataset distribution for sensor-correlation-based methods shows heavy evaluation on SWaT, WADI, GHL, HAI, TE, and power-system benchmarks (IEEE bus systems), with additional evidence from gas-pipeline, GRFICSv2/Factory I/O, and aNormalies-based testbeds.

    “Finally, the dataset distribution indicates that sensor-correlation-based methods are heavily evaluated on SWaT, WADI, GHL, HAI, TE, and power-system benchmarks (IEEE bus systems), with additional evidence from gas-pipeline, GRFICSv2/Factory I/O, and aNormalies-based testbeds [88-96,98-102,104-110].”

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Authors
Ayas, S., Ayas, M. S., Cavdar, B., & Sahin, A. K.
Affiliations
Karadeniz Technical University (TR)
Corresponding author
Ayas, Selen
Venue
Journal of Information Security and Applications
Publisher
Elsevier BV
Published
2025-11
Volume
94
Pages
104206
ISSN
2214-2126 (ISSN-L)
OpenAlex
W4413426279
Cited by
4
References
46
Citations by year
2026: 4
FWCI
1.40
Type
journal article
Language
en
Open access
closed
Locations
Journal of Information Security and Applications (publishedVersion)
Funders
TÜBİTAK (122E383)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Keywords
cyberattack detection, industrial control systems, anomaly detection, water distribution systems, water treatment systems, time series, deep neural network, gated recurrent unit, long short-term memory, recurrent neural networks, convolutional neural networks, SWaT dataset, WADI dataset

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