The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Dataset to Support Research in the Design of Secure Water Treatment Systems
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The 1999 DARPA off-line intrusion detection evaluation
Lippmann, R. P., Haines, J. W., Fried, D. J., Korba, J., & Das, K. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00139-0 · Cited by 877
The UCI KDD archive of large data sets for data mining research and experimentation
Bay, S. D., Kibler, D. F., Pazzani, M. J., & Smyth, P. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/380995.381030 · Cited by 234
An Investigation into the Response of a Water Treatment System to Cyber Attacks
Adepu, S., & Mathur, A. P. (2016) · Proceedings - IEEE International Symposium on High-Assurance Systems Engineering/Proceedings · doi:10.1109/hase.2016.14 · Cited by 88
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Items 701–734 of 734
Indirect Prompt Injection Against LLM Copilots in Industrial Control Systems
Shahid, S., & Mallavrapu, D. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-5039-4_1
Temporal Modeling for Anomaly Detection in Industrial Control Systems
Wang, Z., Ahmed, C. M., Umer, M., & Ranjan, R. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-5039-4_9
Ertekin, H., & Coşkun, A. (2026) · International Journal of 3D Printing Technologies and Digital Industry · doi:10.46519/ij3dptdi.1825452
SHGCN: An Industrial Fault Diagnosis Model Integrating Simplicial Hierarchical Topology
Li, G., & Zhang, W. (2026) · Machines · doi:10.3390/machines14090983
GRACE-ICS: Graph-Aware Conformal Intervals for Industrial Operations Assurance
Avdalović, A., Khoury, J., & Bou‐Harb, E. (2026) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms69089.2026.11668216
“For instance, they inform pump scheduling for energy savings [2], [3], setpoint tuning such as chlorine dosing in response to flow [4], and level or composition planning to avoid process upsets [5].”
InfluentialMehrpouyan, H. (2026) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit68936.2026.11670408
“The SWaT water-treatment testbed and datasets have been particularly influential because they combine long-duration normal operation with diverse labeled cyber-physical attacks, enabling reproducible evaluation of detection methods in a realistic process setting [10].”
InfluentialEpistemic Edge: Subjective Logic Guardrails for LLM-Driven IoT Actuation
Syed, M. M., & Silaghi, M. (2026) · IEEE International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri69576.2026.00017
“Future work will complete the Tier 1 to 4 BATADAL validation with the LLM in the loop, extend to SWaT [19], deploy on edge hardware, and investigate learned ensembles over the three SL dimensions.”
Wang, H., & Ren, Y. (2026) · International Conference on Digital Society and Artificial Intelligence (DSAI) · doi:10.1145/3833541.3833705
Discovering Underlying Pattern Clusters by Encoding Data to Transformer Tokens
Xu, W., Shao, H., & Xie, Z. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3846179
“• Secure Water Treatment (SWaT) [Goh et al. 2017]: Collected by the Singapore University of Technology and Design, the SWaT dataset consists of 51 features, each corresponding to a distinct sensor measurement in the industrial water treatment system.”
AI-Assisted Resilience Monitoring for Industrial Control System Security
Mittal, S., Polurouthu, V. K., & Gupta, A. (2026) · IEEE Access · doi:10.1109/access.2026.3732974
“Public testbeds including SWaT, WADI, BATADAL, Tennessee Eastman, and HAI enabled reproducible cyber-physical anomaly research [5], [6], [7], [8], [9], [10], [11], [12].”
Wavelet-enhanced Mamba and graph network-based model for time series anomaly detection
Xu, H., Ren, J., Liu, J., et al. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03250-x
“Secure Water Treatment Dataset (SWaT) [48] is generated from a scaled-down water treatment testbed designed to simulate real industrial control systems (ICS).”
Long, J., Yan, X., Hong, J., Luo, Y., & Qi, Q. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01284-3
“We evaluate all methods on fi ve public multivariate time-series anomaly detection benchmarks: MSL, SMAP, SWaT, PSM, and SMD (Abdulaal et al. 2021; Goh et al. 2016; Hundman et al. 2018; Su et al. 2019).”
Adaptive Defense Optimization Under Intelligent Data Poisoning Attacks in Industrial Control System
Cai, X. F., Sun, Y., Lu, H. D., Shi, K., Cheng, J. S., Wen, S., & Tian, Z. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3737642
“To provide a more representative basis for characterizing practical attack behavior, this work adopts the SWaT (Secure Water Treatment) dataset [28].”
InfluentialSantos, E. J. C. D., Trindade, A. B., & Cordeiro, L. C. (2026) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcom71387.2026.11703493
“Benchmarking against ICS-specific datasets (SWaT, Morris) [22, 23] remains future work.”
InfluentialTowards Deployable Anomaly Detection in industrial CPS/ICS: A Survey and Deployability Assessment
Kollarou, A., Lin, J., Pirbhulal, S., Lee, M., & Gkioulos, V. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01330-1
Oni, O. M., Efiong, J. E., Akinwale, A., Amoo, A. O., Afolabi, A., Balogun, O. E., & Olajubu, E. A. (2026) · Journal of Information and Communications Technology Algorithms Systems and Applications · doi:10.64189/ict.26318
Anomaly Detection in Industrial Assets Through Unsupervised Multi-ensemble Model
Trapani, N., Longo, L., Manno, G., et al. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-38614-4_7
Sajjadi, P., Dinmohammadi, F., & Shafiee, M. (2026) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac70325.2026.11712535
“To evaluate the proposed methodology, the Secure Water Treatment (SWaT) A6 dataset, developed by iTrust at the Singapore University of Technology and Design [21] is used.”
GCAT: a graph-convolutional adversarial transformer for multivariate time series anomaly detection
Sefati, S. T., Razavi, S. N., & Salehpoor, P. (2026) · Cluster Computing · doi:10.1007/s10586-026-06635-3
A Precision-First Multi-View Temporal Anomaly Detection Framework for Cyber-Physical Systems
Komara, Y. K. S. C., Reddy, P. J. S. P., Kommalapati, N. S., Vayya, V. P., & Singh, S. (2026) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci71772.2026.11710480
“The SWaT data set [14] is derived from a small-scale model of the water treatment test bed developed by Singapore University of Technology and Design.”
On-Device Is Not Private: Measuring Membership Leakage in Lightweight Sensor Anomaly Detectors
Panesar, M. S., Kataria, A., Rani, S., Kataria, A., & Puri, V. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8100320
Amer, M. S., Mokhtar, G., Bassam, Y., et al. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11716994
“Work in ICS environments—Ahmed et al. [20], Goh et al. [21], and Kravchik and Shabtai [22]—consistently found that process-aware detection is necessary for SCADA contexts, which motivated our HAI-20.”
Addressing Methodological Challenges in Time Series Anomaly Detection
Xu, N., & Boström, H. (2026) · Machine Learning · doi:10.1007/s10994-026-07183-y
“This dataset records 123 variables over 16 days of operation, 14 running normally and 2 with attacks. iTrust has stated that the SWaT system required five hours to reach it’s normal operating state (Goh et al., 2017), therefore we have truncated those first five hours.”
Evaluating and Mitigating Cyber Exploits within Smart Grid Advanced Metering Infrastructure
Jain, S., Dwivedi, S. V., Varshney, S., et al. (2026) · International Conference on Innovative Sustainable Computational Technologies (CISCT) · doi:10.1109/cisct69589.2026.11726939
“While the L 2 filter picks out gross changes, the SVM picks out subtle, low-intensity changes that operate within normal limits [13].”
DE-SVDD: dual-encoder SVDD for multivariate time-series anomaly detection
Xie, X., Zhang, H., Liu, K., Wu, M., & Wan, T. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07539-w
“…widely used for multivariate time-series anomaly detection are adopted: SWaT (Secure Water Treatment) [27], SMD (Server Machine Dataset) [11], WADI (Water Distribution) [28], MSL (Mars Science Laboratory), SMAP (Soil Moisture Active Passive satellite) [29], and PSM (Pooled Server Metrics) [30].”
Protocol-Constrained Adversarial Evasion Against Transformer-Based Network Intrusion Detection
Ghosh, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0363-3.ch004
Rahmani, J., Çakmakcç, S. D., Detken, K.-O., et al. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11725122
Manca, G., Maag, B., Sivanthi, T., Guo, S., Sommer, P., & Fay, A. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11724982
“SWaT [24,25] and WADI [26] are prominent examples because they combine physical process behavior with documented cyber attack scenarios and have become widely used references for industrial intrusion-detection evaluation.”
FSA-AD: Frequency-Semantic Anomaly Detection with Adaptive Patching and Orthogonal Prototypes
Liu, L., Qian, Y., Qi, Q., Long, J., & Luo, Y. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729090
Zhao, H., Wang, Z., Teng, Y., et al. (2026) · doi:10.1109/ijcnn69854.2026.11728627
“E XPERIMENT 1) Datasets: We used six popular benchmarks in recent SOTA studies: SWaT [25], with 51 channels from a water treatment facility with attack instances; WADI [26], a water distribution system with 123 sensors in 2017,2019; PSM [27], with 25 expert-labeled metrics from eBay’s server…”
Mi, S., Wu, X., Zhang, S., et al. (2026) · doi:10.1109/ijcnn69854.2026.11729386
“Experiments are conducted on two real-world datasets collected from large-scale testbeds, which are standard benchmarks for ICPS security research: SWaT (Secure Water Treatment) [14]: A six-stage water treatment testbed.”
Automated ICS Attack Simulation and Dataset Generation for AI-Based Intrusion Detection
Chabanov, I., Pavlychev, A., & Kosach, L. (2026) · International Ural Conference on Electrical Power Engineering (UralCon) · doi:10.1109/uralcon71028.2026.11734745
“The BATADAL dataset [4] targets water distribution networks and covers operational anomalies, while the SWaT dataset [5] from iTrust addresses secure water treatment scenarios.”
Generative Adversarial Imputation With Relation‐Aware Self‐Attention for Robust Data Recovery
Li, Q., Liu, Y., Yang, X., et al. (2026) · Expert Systems · doi:10.1111/exsy.70420
Liu, W., Liu, M., Xia, F., Qiuyuan, Z., Wei, S., & Yang, W. (2026) · Journal of Engineering and Applied Science · doi:10.1186/s44147-026-01290-2
“To show the effect of violating the first condition, we also report SWaT (Secure Water Treatment) [9, 23], a water treatment testbed with 51 sensors and actuators and time-step labels, on which 72% of the normal test time steps lie outside the range of the training data (allowing a 5% margin).”
Metadata
- Authors
- Goh, J., Adepu, S., Junejo, K. N., & Mathur, A.
- Affiliations
- Singapore University of Technology and Design (SG)
- Corresponding author
- Goh, Jonathan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 88-99
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2768947629
- Semantic Scholar
- 26f058a2aedf8bd24ad5cb97170173b5124dbbf8
- Cited by
- 729
- References
- 3
- Citations by year
- 2026: 109 · 2025: 122 · 2024: 122 · 2023: 115 · 2022: 72 · 2021: 76 · 2020: 54 · 2019: 33 · 2018: 20 · 2017: 6
- FWCI
- 47.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- 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 › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- cyber-physical systems, water treatment plant, cyberattacks, physical assault, network traffic, ultrafiltration, reverse osmosis, industrial control security
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