Detecting cyberattacks based on deep neural network approaches in industrial control systems
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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].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [90]
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].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [90]
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Alarm-Budgeted Event-Level Evaluation Of Ics Anomaly Detection: Lessons From Swat And Wadi
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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
- Licence
- 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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