Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning
Open accessAbstract
As more and more devices in Cyber-Physical Systems (CPS) are connected to the Internet, physical components such as programmable logic controller (PLC), sensors, and actuators are facing greater risks of network attacks, and ... | Find, read and cite all the research you need on Tech Science Press
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Process data are particularly suitable for attacks that perturb the physical process state while keeping network-level statistics relatively unchanged, such as false data injection, malicious command or parameter manipulation, process-oriented attacks, and stealthy drifts in sensor trajectories.
“First, process data are particularly suitable for attacks that perturb the physical process state while keeping network-level statistics relatively unchanged-such as false data injection, malicious command or parameter manipulation, process-oriented attacks, and stealthy drifts in sensor trajectories [67,69-73,75,76].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [67]
The Introduction describes false sensor-data injection and tampered PLC control logic that can alter physical processes. It does not say that these attacks leave network-level statistics relatively unchanged, nor does it discuss stealthy drifts in sensor trajectories. The cited paper therefore supports the process-attack examples, but not the full characterization. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe distribution of datasets shows a strong reliance on SWaT and WADI for benchmarking prediction-residual-based methods, with additional evidence from custom-built avionics, robotics, temperature-control, offshore wind farm, and simulation-based CPS testbeds.
“Finally, the distribution of datasets shows a strong reliance on SWaT and WADI for benchmarking prediction-residual-based methods, with additional evidence from custombuilt avionics, robotics, temperature-control, offshore wind farm, and simulation-based CPS testbeds [67-73,75,76].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [67]
Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIn many works, a model predicts the next-step sensor or control-signal values and computes prediction residuals, or derives an anomaly score that functions as a probabilistic or density-based residual, so that small but systematic deviations in process dynamics can be detected even when they are not visible at the network layer.
“In many of these works, a model predicts the next-step sensor or control-signal values and computes prediction residuals, or derives an anomaly score that functions as a probabilistic or density-based residual, so that small but systematic deviations in process dynamics can be detected even when they are not visible at the network layer [67,69,70,72,73,75].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [67]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe DL-AD method can detect anomaly sequential changes quickly and accurately even in large-scale ICS environments through distributed processing.
“Through distributed processing, the method can detect anomaly sequential changes quickly and accurately even in large-scale ICS environments.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [67]
Tang et al. [67] proposed an anomaly detection method based on Distributed Linear Deep Learning (DL-AD) using SWaT and WADI datasets.
“Using the SWaT and WADI datasets, Tang et al. [67] proposed an anomaly detection method based on Distributed Linear Deep Learning (DL-AD).”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 20 · Cited by 3
Research works printed but not linked: 21
- Lee and A. Edward, "Cyber physical systems: Design challenges," in Proc. 2008 11th IEEE Int. Symp. Object Comp.-Oriented Real-Time Distrib. Comput. (ISORC), Orlando, FL, USA, IEEE, 2008, pp. 363-369.
- Case D U, Analysis of the Cyber Attack on the Ukrainian Power Grid. Washington, DC, USA: Electricity Information Sharing and Analysis Center (E-ISAC), 2016, pp. 1-22.
- C. James, A. Rubin, and L. Watkins, "Don't drink the cyber: Extrapolating the possibilities of Oldsmar's water treatment cyberattack," in Proc. 2022 Int. Conf. Cyber Warf. Secur., New York, 2022, vol. 17, no. 1.
- R. Langner, "Stuxnet: Dissecting a cyberwarfare weapon," IEEE Secur. Priv., vol. 9, no. 3, pp. 49-51, 2011. doi: 10.1109/MSP.2011.67. · DOI · Paper page
- H. Gao, B. Qiu, R. J. D. Barroso, W. Hussain, Y. Xu and X. Wang, "TSMAE: A novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder," IEEE Trans. Netw. Sci. Eng., vol. 10, no. 5, pp. 2978-2990, 2022. doi: 10.1109/TNSE.2022.3163144. · DOI
- J. Lee, The Revolutionary Transformation and Value Creation in Industry 4.0 Era. Beijing: China Machine Press, 2015.
- J. Kim, J. H. Yun, and H. C. Kim, "Anomaly detection for industrial control systems using sequence- to-sequence neural networks," in Comp. Secur.: ESORICS 2019 Int. Workshops, Luxembourg City, Luxembourg, Sep. 26-27, 2020, pp. 3-18.
- T. Zhang et al., "When moving target defense meets attack prediction in digital twins: A convolutional and hierarchical reinforcement learning approach," IEEE J. Sel. Areas Commun., vol. 41, no. 10, pp. 3293-3305, 2023. doi: 10.1109/JSAC.2023.3310072. · DOI
- Y. Yin et al., "IGRF-RFE: A hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset," J. Big Data, vol. 10, no. 1, 2023, Art. no. 15. doi: 10.1186/s40537-023-00694-8. · DOI
- E. Mushtaq, A. Zameer, and A. Khan, "A two-stage stacked ensemble intrusion detection system using five base classifiers and MLP with optimal feature selection," Microprocess. Microsyst., vol. 94, no. 104660, 2022, Art. no. 104660. doi: 10.1016/j.micpro.2022.104660. · DOI
- A. Zeng, M. Chen, L. Zhang, and Q. Xu, "Are transformers effective for time series forecasting?" in Proc. AAAI Conf. Artif. Intell., Washington, DC, USA, 2023, vol. 37, pp. 11121-11128. doi: 10.1609/aaai.v37i9.26317. · DOI
- V. Ekambaram, A. Jati, N. Nguyen, P. Sinthong, and J. Kalagnanam, "TSMixer: Lightweight mlp-mixer model for multivariate time series forecasting," in Proc. ACM SIGKDD Int. Conf. Know. Disc. Data Min., Xi'an, China, 2023, pp. 459-469.
- H. Li, H. Xu, W. Peng, C. Shen, and X. Qiu, "Multi-scale sampling based MLP networks for anomaly detection in multivariate time series," in Proc. 29th IEEE Int. Conf. Parallel Distrib. Syst. (ICPADS), China, Ocean Flower Island, 2023, pp. 1421-1428.
- K. He, X. Chen, S. Xie, Y. Li, P. Dollár and R. Girshick, "Masked autoencoders are scalable vision learners," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., New Orleans, LA, USA, 2022, pp. 16000-16009.
- Y. Fu and F. Xue, "MAD: Self-supervised masked anomaly detection task for multivariate time series," in Int. Joint Conf. Neural Netw. (IJCNN), Padua, Italy, 2022, pp. 1-8.
- K. Zhao, X. Zhao, Z. Zhang, and M. Li, "MAE4Rec: Storage-saving transformer for sequential recom- mendations," in Proc. 31st ACM Int. Conf. Inform. Know. Manag., Atlanta, GA, USA, 2022, pp. 2681-2690.
- P. Tang and X. Zhang, "MTSMAE: Masked autoencoders for multivariate time-series forecasting," in IEEE 34th Int. Conf. Tools with Artif. Intell. (ICTAI), Macao, 2022, pp. 982-989.
- C. Zhang, T. Zhou, Q. Wen, and L. Sun, "TFAD: A decomposition time series anomaly detection architecture with time-frequency analysis," in Proc. 31st ACM Int. Conf. Inform. Know. Manag., Atlanta, USA, 2022, pp. 2497-2507.
- Á.L. PeralesGómez, L. Fernández Maimó, A. H. Celdrán, and F. J. García Clemente, "Madics: A methodology for anomaly detection in industrial control systems," Symmetry, vol. 12, no. 10, 2020, Art. no. 1583. doi: 10.3390/sym12101583. · DOI
- C. M. Ahmed, V. R. Palleti, and A. P. Mathur, "WADI: A water distribution testbed for research in the design of secure cyber physical systems," in Proc. 3rd Int. Workshop Cyber-Phy. Sys. Smart Water Netw., PA, USA, 2017, pp. 25-28.
- D. Nedeljkovic and Z. Jakovljevic, "CNN based method for the development of cyber-attacks detection algorithms in industrial control systems," Comput. Secur., vol. 114, no. 2, 2022, Art. no. 102585. doi: 10.1016/j.cose.2021.102585. · DOI
- Authors
- Tang, S., Ding, Y., & Wang, H.
- Affiliations
- Guilin University of Electronic Technology (CN)
- Venue
- Computers, Materials & Continua
- Publisher
- Tech Science Press
- Published
- 2025
- Volume
- 82
- Issue
- 1
- Pages
- 1129-1150
- ISSN
- 1546-2218 (ISSN-L)
- 1546-2226
- OpenAlex
- W4404618050
- Cited by
- 3
- References
- 20
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.50
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Computers, materials & continua/Computers, materials & continua (Print) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (62172119)
- Natural Science Foundation of Guangxi Province (2024GXNSFDA010064, 62172119, 2023GXNSFAA026236)
- Singapore University of Technology and Design
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- cyber-physical systems, network attacks, programmable logic controller, intrusion detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
