Anomaly detection for a water treatment system based on one-class neural network
Open accessAbstract
The prevalence of Internet-of-Things (IoT) technologies and the ubiquity of networked sensors and actuators in many industrial control systems (ICS) have led to the exposure of critical infrastructure in our society to malicious activities and cyber threats. Programmable logic controllers (PLCs) are embedded devices that automate ICS processes. PLCs, which serve as the heart of ICS, are vulnerable to attacks and system malfunctions like other embedded devices. Because PLCs are widely used to control ICS physical processes, attacks against PLCs can cause irreparable damage to enterprises and even loss of life. However, due to the unique and proprietary architecture of PLCs, it is not easy to apply traditional tools and techniques for PLC protection. This work presents an unsupervised learning approach for anomaly detection in ICS based on neural networks with one class objective function and additional regularization term. This technique combines the abilities of neural networks to learn complex relationships with a one-class objective function and a regularization term for separating normal conditions from anomalous operations. The newly introduced regularization term provides a model-tuning mechanism based on specific industrial requirements and performance metrics of interest (i.e., precision or recall). The model was evaluated on a recent real-world ICS dataset: the Secure Water Treatment (SWaT) dataset. The proposed technique’s performance is compared with previous work, showing improvements in terms of scalability and attack detection capability, proving that the proposed technique is suitable for use in real ICS scenarios. The proposed method with the regularization term demonstrated superior recall values in 15 out of the 36 attack scenarios in the SWaT dataset, which is the largest of any published methods in the literature. A qualitative analysis of the proposed technique on the SWaT security showdown event data further proves the technique’s high anomaly detection ability on real-time injected attacks.
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Reconstruction-error-based methods are particularly effective for detecting sensor/actuator faults, false data injection, command manipulation, and generic injection-type attacks that cause local or global distortions in multivariate process trajectories.
“First, these methods are particularly effective for detecting sensor/actuator faults, false data injection, command manipulation, and generic injection-type attacks that cause local or global distortions in multivariate process trajectories [77,79,81-83,86,87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [81]
The Method section reports detection of several SWaT attacks, including an actuator attack, and the Conclusion reports improved recall for some attack scenarios. But the Anomaly Detection Method section describes a neural network with a one-class objective, not a reconstruction-error-based method. The paper therefore does not establish the claimed effectiveness of reconstruction-error methods or the proposed explanation in terms of local or global trajectory distortions. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe dataset distribution for reconstruction-error-based anomaly detection studies shows a strong reliance on a limited set of public process benchmarks (SWaT, HAI, BATADAL, and TE) supplemented by a small number of custom-built CAN fieldbus and wind-turbine SCADA datasets.
“Finally, the dataset distribution again reveals a strong reliance on a limited set of public process benchmarks-SWaT, HAI, BATADAL, and TE-supplemented by a small number of custom-built CAN fieldbus and wind-turbine SCADA datasets [77,79,81-83,85-87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [81]
The System Description and Data Preprocessing sections (page unavailable) confirm that this study evaluates anomaly detection on SWaT. But the Anomaly Detection Method section describes a one-class neural-network objective, not reconstruction-error detection. The paper does not establish the claimed distribution across HAI, BATADAL, TE, CAN fieldbus, and wind-turbine SCADA datasets. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSome approaches rely almost exclusively on reconstruction error derived from AE or VAE models trained on normal process data, while others treat boundary distances in one-class neural networks or PCA-residual statistics as functionally equivalent 'reconstruction-like' anomaly measures.
“Some approaches rely almost exclusively on reconstruction error derived from AE or VAE models trained on normal process data [77,79,83], while others treat boundary distances in one-class neural networks or PCA-residual statistics as functionally equivalent 'reconstruction-like' anomaly measures [81,86].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [81]
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 measuredBoateng et al. [81] presented an anomaly detection method based on a One-Class Neural Network (OC-NN) using the SWaT dataset.
“Using the SWaT dataset, Boateng et al. [81] presented an anomaly detection method based on a One-Class Neural Network (OC-NN).”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [81]
Autoencoder-like models (AE, VAE, OC-NN) and PCA-based approaches can flag process states that lie outside the learned normal manifold, including zero-day attack scenarios that were not explicitly seen during training.
“By learning a compact representation of normal operating conditions and monitoring reconstruction error or boundary-based anomaly scores, autoencoder-like models (AE, VAE, OC-NN) and PCA-based approaches can flag process states that lie outside the learned normal manifold, including zero-day attack scenarios that were not explicitly seen during training [77,79,81-83,86,87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [81]
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Deep Learning for Detecting Anomalies in Water Treatment Systems
Rajan, S., G, V. V., S, S. M., Tamilselvam, G., & Vimalathethan, I. (2026) · International Conference on Intelligent Computing, Networks, and Security (IC-ICNS) · doi:10.1109/ic-icns68863.2026.11537743
RAID: Resilient AI Defense Model for Water Utility Cyber–Physical Attacks
Gaber, T., Hamed, A., Nicho, M., & Tharwat, A. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3722599
LFTS-AE: Leakage-Safe IIoT Anomaly Detection with EVT Calibration and Selective Three-Way Decisions
Al–Hussaini, K., & Al-Nharai, W. A. (2026) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta70636.2026.11652130
A Group-Aware Temporal Autoencoder for Anomaly Detection in Water Treatment Systems
Abdullahi, B., & Vadera, S. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-5039-4_12
Pandey, R. K., Das, T. K., Gangopadhyay, S., & Zhou, J. (2026) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2026.100906
AI-Driven Anomaly Detection for Securing U.S. Critical Infrastructure Networks
Olatunbosun, E., Bizel, G., & Lacava, A. (2025) · International Journal of Scientific Research in Artificial Intelligence and Machine Learning · doi:10.32628/ijsraiml251309
Pérez-Pérez, E.-J. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7100564
- Authors
- Aboah Boateng, E., Bruce, J. W., & Talbert, D. A.
- Affiliations
- Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USA
- Tennessee Technological University (US)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 115179-115191
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4312226897
- Cited by
- 44
- References
- 46
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 14 · 2023: 10
- FWCI
- 3.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- anomaly detection, industrial control systems, SWaT dataset, programmable logic controller, cyberattack detection, regularization, unsupervised learning, water treatment systems, Internet of Things security, critical infrastructure protection
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