Cited by 734Smart Grid Security and ResilienceNetwork Security and Intrusion DetectionFault Detection and Control Systems

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A Dataset to Support Research in the Design of Secure Water Treatment Systems

Goh, Jonathan, Adepu, Sridhar, Junejo, Khurum Nazir, Mathur, Aditya·2017·Lecture Notes in Computer Science

DOI 10.1007/978-3-319-71368-7_8

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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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Run-time integrity monitoring of untrustworthy analog front-ends
Salem, H., & Topham, N. P. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10137087 · Cited by 1
“…ECG signal from the ECGSYN tool-set [9], readings of flow and level sensors from the secure water treatment system developed by iTrust in the centre for Research in Cyber Security at Singapore University of Technology and Design [10], and level and flow readings of airplane fuel tank sensors [11].”
Method
Methodology for Dataset Generation for Research in Security of Industrial Water Treatment Facilities
Novikova, E. S., Владимировна, Ф. Е., & Saenko, I. B. (2023) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon58002.2023.10272930 · Cited by 1
“An example of such a testbed is the SWaT dataset [5].”
MethodInfluential
A Data-Driven Cyber Resilience Assessment for Industrial Plants
Simone, F., Cilli, C., Gravio, G. D., et al. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-45642-8_46 · Cited by 1
Graph Transformer: Anomaly Prediction and Interpretation in Multivariate Time Series
Li, J., Liu, C., & Ding, J. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650606 · Cited by 1
PASTA : Neural Architecture Search for Anomaly Detection in Multivariate Time Series
Trirat, P., & Lee, J.-G. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3508845 · Cited by 1
“SWaT [109] is the most widely-used 1-entity dataset for TSAD collected from a real-world secure water treatment plant.”
OptAML: Optimized adversarial machine learning on water treatment and distribution systems
Ayas, M. Ş., Kara, E., Ayas, S., & Şahin, A. K. (2025) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2025.100740 · Cited by 1
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HTM-CDFK: An Online Industrial Control Anomaly Detection Algorithm Based on Hierarchical Time Memory
Liu, J. L., Zhang, Y., Wang, Z., Ren, Y., Chen, L., & Chen, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3549741 · Cited by 1
“SWaT [38] is a widely dataset collected from a water treatment testbed, simulating an ICS environment.”
Distributional reservoir state analysis for real-time anomaly detection in multivariate time series data
Tamura, H., Fujiwara, K., Aihara, K., & Tanaka, G. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00090-6 · Cited by 1
Anomaly Detection in Water Treatment System Data Using Kohonen's Self-Organising Neural Network
Дмитриевна, П. А., Sokolov, A. N., & Ruchay, A. N. (2025) · International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) · doi:10.1109/icieam65163.2025.11028287 · Cited by 1
“All experiments were performed on the Secure Water Treatment (SWaT) dataset [7].”
Res2coder: A two-stage residual autoencoder for unsupervised time series anomaly detection
Wang, H., Liu, Y., Yin, H., Zheng, X., Zha, Z., Lv, M., & Guo, Z. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06684-y · Cited by 2
“The Secure Water Treatment (SWaT) [ 15 , 31] dataset is a simulation of a water treatment system proposed by the Singapore University of Technol - ogy and Design (SUTD) for testing the security of Industrial Control Systems (ICSs).”
Enhancing Anomaly Detection in Multivariate Time Series with Hybrid Deep Learning Architecture
Wang, J. (2025) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice64954.2025.11064389 · Cited by 1
ModRTU InjectX: A Command Injection Simulation Tool for Industrial Cybersecurity Research
Dobrády, Z., Nagy, S., & Hidvégi, T. (2025) · International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) · doi:10.1109/icest66328.2025.11098380 · Cited by 1
“Among the traffic datasets widely used by the research community for ICS/SCADA systems are the Secure Water Treatment (SWaT) testbed and the ICS-Data repository [9].”
Cross-Domain Evaluation of CNN-Based and Generative Adversarial Networks Models’ Generalisability for (D)DoS Attack Detection in CPS and IoT
Ngo, V., Yongchareon, S., Ruan, J. S., Mohaghegh, M., & Sinha, R. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-7036-9_2 · Cited by 1
Fault Tolerance vs. Attack Detection in Industrial Control Systems: A Deep Learning Approach
Mehrpouyan, H. (2025) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr64739.2025.11130080 · Cited by 1
“Although real-world datasets such as SWaT [5] and WADI [8] capture industrial scenarios, they are domain-specific and do not explicitly explore the relationship between fault masking and attack detection.”
AAAD: Asynchronous Inter-Variable Relationship-Aware Anomaly Detection for Multivariate Time Series
Liu, H., Huang, X., Jia, M., Zhang, L., Jia, T., Wu, Z., & Li, Y. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210144 · Cited by 1
“…detecting factory equipment failures from audio data [1], identifying crimes from video data [2], monitoring failures in cyber-physical systems such as aerospace [3] and water treatment plants [4], and diagnosing faults in software systems (e.g., cloud services) using performance metrics [5]–[7].”
Intelligent Intrusion Detection System for Cybersecurity of Water Utilities
Mesca, S., & Stâmâtescu, G. (2025) · RoEduNet Conference: Networking in Education and Research (RoEduNet) · doi:10.1109/roedunet68395.2025.11208394 · Cited by 1
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Manivannan, D. (2025) · IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) · doi:10.1109/cscloud66326.2025.00053 · Cited by 1
“Other notable datasets include the SWaT dataset [12] from a water treatment testbed, the N-BaIoT dataset [13] for IoT Botnet attacks, and the CSE-CIC-IDS2018 [14] and CICIOT2023 [15] datasets for cybersecurity analysis.”
Contrastive Learning for Robust Time Series Anomaly Detection in Cyber-Physical Systems
Ma, Y., Wang, H., Guan, H., Tao, X., & Yin, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9849-3_29 · Cited by 1
Quantum Machine Learning for Anomaly Detection in IoT Devices
Chowdhury, S. A., Karim, S. F., Bhuiyan, M. S. I., Shuvro, A. A., & Hossen, M. S. (2025) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1109/aimlsystems67835.2025.11331191 · Cited by 1
“Among these attempts, the LSTM autoencoder [8] with feature extraction using graph convolution [9] became a standard, giving great results on benchmark datasets like KDDCUP99 [10], secure water treatment (SWaT) [11] and the Gas pipeline system [12] dataset.”
Anomaly Detection in CPS using LSTM-Autoencoder with OCSVM on SWaT Dataset
Fiadzeawu, D. P., Agyapong, D., Ranganathan, P., et al. (2025) · doi:10.1109/cars67163.2025.11337908 · Cited by 1
“The dataset was acquired from a scaled-down industrial water treatment testbed developed at the Singapore University of Technology and Design, to emulate real-world CPS dynamics [8].”
Secure aggregation for heterogeneous enterprise data based on federated meta-learning
Yang, C., & Ma, Y. (2026) · Scientific Reports · doi:10.1038/s41598-025-34735-4 · Cited by 1
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Park, S., Lee, G., & Ko, Y. (2025) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc66702.2025.11387870 · Cited by 1
“A. Experimental Setup 1) Datasets: We use two real-world industrial time-series datasets: SWaT [9] and HAI [10].”
Attack pattern mining to discover hidden threats to industrial control systems
Umer, M., Ahmed, C. M., Mathur, A. P., et al. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01236-y · Cited by 1
“The 36 attacks generated by the domain experts in [12] served as the foundation of our approach to generate attack patterns.”
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NER-AD: Noise-Robust Reconstruction Enhanced by Representation-Learning for Metric Anomaly Detection in Online Service Systems
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“SWaT (Secure Water Treatment) [44] is a monitoring dataset of a secure (containing50variables).”
Take Attention as Gate: An Associative Recurrent Network-Based Intrusion Detection Method for Industrial Control Network
Liu, Z., Ye, D., Yang, C., Ding, Y., Liu, Y., Tang, L., & Chen, C. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3688982 · Cited by 1
“Reference [43].”
From Scores to Alarms: NP-Calibrated Event-Level Detection with a Multi-horizon Digital Twin for ICS
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SIAD: a low-latency AI-driven software architecture for anomaly detection in industrial IoT environments
Asad, M., & Alhasawi, Y. (2026) · Cluster Computing · doi:10.1007/s10586-026-06201-x · Cited by 1
“We employed two publicly available and widely used IIoT datasets for anomaly detection tasks: ● SWaT Dataset (Secure Water Treatment) [39]: Collected from a testbed simulating a water treatment plant, this dataset contains multivariate time-series data from 51 sensors and actuators.”
POSTER
Choi, S., Lee, W., Shin, H.-K., et al. (2018) · doi:10.1145/3196494.3201589 · Cited by 1
“Secure Water Treatment (SWaT)[1], a CPS test environment in the water treatment domain, was configured as a physical environment that reflects the complex operating environment of an actual site with a pump, sensor, and actuator, similar to a real device, to implement the control process.”
Method
Generating Abnormal Industrial Control Network Traffic for Intrusion Detection System Testing
Song, J.-Y., Lee, W., Yun, J.-H., et al. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-04537-1_14 · Cited by 1
“In the ﬁeld of industrial control systems, some datasets have been made available, including for secure water treat-ment [6], S7Comm [18] and Modbus [13].”
Modification of Recursive Kalman Filter Algorithm for Adaptive Prediction of Cyber Resilience for Industrial Systems
Lavrova, D. S., & Петрович, З. Д. (2020) · Nonlinear Phenomena in Complex Systems · doi:10.33581/1561-4085-2020-23-3-270-279 · Cited by 1
Asymptotic Random Distortion Testing and Application to Change-in-Mean Detection
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Exploiting Time Dynamics for One-Class and Open-Set Anomaly Detection
Brigato, L., Sartea, R., Simonazzi, S., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87897-9_13 · Cited by 1
Explainable sensor data-driven anomaly detection in Internet of Things systems
Hussain, M. T., & Perera, C. (2022) · ORCA Online Research @Cardiff (Cardiff University) · doi:10.1109/iotdi54339.2022.00021
Application of Danger Theory for Anomaly Detection on Technological Data
Bredikhin, B. A., & Chernyshov, Y. (2023) · IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) · doi:10.1109/usbereit58508.2023.10158852 · Cited by 1
“The proposed algorithm is evaluated on the SWaT dataset [2].”
Method
Machine learning and cyber security
Karius, S., Knöchel, M., Heße, S., & Reiprich, T. (2023) · it - Information Technology · doi:10.1515/itit-2023-0050 · Cited by 1
Next Generation Cyber-Physical Architecture and Training
Shrivastava, S., & Mathur, A. P. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-30191-9_13 · Cited by 1
Self-citation
Constructing Cyber-Physical System Testing Suites Using Active Sensor Fuzzing
Zhang, F., Wu, Q., Xuan, B., Chen, Y., Lin, W., POSKITT, C. M., Sun, J., & Chen, B. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3309330 · Cited by 2
“Second, and in contrast, we compare against the attacks of an established, expert-crafted benchmark [29], which were systematically derived from an attack model.”
BackgroundMethodResultInfluential
Attacks Detection in Industrial Cyber-Physical Systems Using Convolutional Neural Networks
Salah, M., & Elshenawy, L. M. (2023) · International Conference on Electronic Engineering (ICEEM) · doi:10.1109/iceem58740.2023.10319541 · Cited by 1
Edge Perception Temporal Data Anomaly Detection Method Based on BiLSTM-Attention in Smart City Big Data Environment
Xia, B., Zhou, J., Kong, F., et al. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624502141 · Cited by 1
Industrial Control System Modeling Based on Knowledge Graph
Teng, J., Yang, R., Wang, T., Feng, H., Cao, Y., & Yan, J. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10451881 · Cited by 1
A Deep Detection Method of Abnormal State of Industrial Control System Based on Hierarchical Clustering Analysis
Zhang, Z., Zhang, X., Wang, R., et al. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-4522-7_12 · Cited by 1
A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network
Yuan, X., Ning, K., Wei, K., He, Y., Zhu, Q.-X., Zhang, M., Luo, Y., & Zhang, Y. (2024) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls61622.2024.10606793 · Cited by 1
“The details about 3 datasets are given as follows: Secure Water Treatment (SWaT) dataset is collected from a water treatment testbed coordinated[12].”
Method
Data Distribution-Based Change Detection Framework in SWaT Security Monitoring
Hekfusz, M., Mahajan, V., Kamuzora, A., et al. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-70259-4_2 · Cited by 1
Grouped Graph Neural Networks for Anomaly Detection in Time Series
Guo, Z., Kong, W., & Liu, Y. (2024) · doi:10.1109/ijcnn60899.2024.10649927 · Cited by 1
“SWaT [9] is a dataset collected from a real-world water treatment plant with 7 days of normal and 4 days of abnormal operation.”
Background
A Deep Learning Approach for Anomaly Detection for Industrial Control Systems
Giracca, D. M., Pires, F. L., Barán, B., & Jara, E. A. M. (2024) · Latin American Computer Conference (CLEI) · doi:10.1109/clei64178.2024.10700451 · Cited by 1
“Finally, an experimental evaluation of the V-detector proposed in [10] was conducted using the SWaT dataset [7], achieving an accuracy of 0.8500, as summarized in Table 1.”
MethodInfluential
Optimal Sensor Data Resampling for Anomaly Detection in Industrial Control Systems
Birihanu, E., & Lendák, I. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-3442-9_49 · Cited by 1
The Good, the Bad and the Ugly: Investigating the Effectiveness of Graph Deep Neural Networks for Anomaly Detection in Industrial Control Systems
Nahalka, M., Cook, M. M., & Pezaros, D. P. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-81900-1_2 · Cited by 1
Anomaly Detection and Factor Estimation Using Graph Neural Network with Storage Battery
Shinozaki, Y., Yoshikawa, J., & Yoshida, W. (2024) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera62673.2024.10815530 · Cited by 1
“SWaT 1 is the time-series dataset collected from a testbed simulating a water treatment facility (Secure Water Treatment, SWaT) [5] (TABLE 1) consists of the first 7 days operating normally, serving as training data where all points are normal, and the latter 6 days, where 36 anomalies are…”
A Self-adaptive Hybrid Network Anomaly Detection Engine
Bagha, A. M., Woungang, I., Traoré, I., et al. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108248 · Cited by 1
Enhancing Real-Time Anomaly Detection of Multivariate Time Series Data via Adversarial Autoencoder and Principal Components Analysis
Ali, A. H., Almisbahi, H., Alkayal, E., & Almakky, A. (2025) · Electronics · doi:10.3390/electronics14153141 · Cited by 3
RENEWABLE ENERGY INFRASTRUCTURE: VULNERABILITY MAPPING AND PREDICTIVE RISK MODELING
Varbanov, V., & Atanasova, T. V. (2025) · International Multidisciplinary Scientific GeoConference SGEM ... · doi:10.5593/sgem2025/4.1/s16.18 · Cited by 1
Abnormal Traffic Detection of Industrial Edge Network Based on Deep Nature Learning
Liu, Q., Zhang, B., Zhao, J., Zang, C., Wang, X., & Li, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78612-0_50 · Cited by 1
Tool Paper: Time Series Anomaly Detection Platform for MATLAB Simulink
Ding, S., Ayoub, S., & Morozov, A. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15842-1_15 · Cited by 1
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Shi, L., Krishnan, S., Wen, S., & Xiang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23020-2_19 · Cited by 1
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Liu, Y., Han, Y., & An, W. Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17551-0_27 · Cited by 1
Industrial Internet of Things (IIoT): Testbed and Datasets for CyberSecurity and Digital Forensics
Mekala, S. H., Baig, Z. A., & Anwar, A. (2022) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon56628.2022.10083609 · Cited by 1
“another ICS dataset proposed by [11] that represents various attacks against Modbus protocol.”
Background
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning AttacksPreprint
Kravchik, M., & Shabtai, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02741 · Cited by 1
Unsupervised Learning Based Robust Multivariate Intrusion Detection System for Cyber-Physical Systems using Low Rank MatrixPreprint
Dutta, A. K., Mukhoty, B., & Shukla, S. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.02930 · Cited by 1
“The SWaT dataset [9] comprises of reading from the sensors and actuators of an operational water treatment testbed, at different timestamp.”
Method
Proceedings of the 9th ACM Cyber-Physical System Security Workshop
(2023) · doi:10.1145/3592538 · Cited by 1
Intrusion Detection Datasets for Cyber-Physical Systems: Taxonomy, Challenges, and OpportunitiesPreprint
Quincozes, V. E., Quincozes, S. E., Neves, C. D. A. V., Passos, D. G., & Mossé, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5247519 · Cited by 1
Unsupervised anomaly detection in time-series
Audibert, J. (2021) · theses.fr (ABES) · doi:10.70675/92cd8d0bz0d6dz4c3cza4e4z859decdbbd88 · Cited by 6
Identifying and Detecting Attacks in Industrial Control Systems
Tuptuk, N. (2019) · UCL Discovery (University College London)
Do’s and Don’ts of Distributed Intrusion Detection for Industrial Network Topologies
Schneider, P. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006117
An Investigative Study on Minimising Security Attacks using Deep learning in a Cyber Physical System
Yogi, M. K., & Chakravarthy, A. S. N. (2021)
Runtime Enforcement of Programmable Logic ControllersPreprint
Ruggero Lanotte, A., Merro, M., & Munteanu, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.10668
“The SWaT system has been used to provide a dataset containing physical and network data recorded during 11 days of activity [19].”
BackgroundMethod
Data-driven discovery of mode switching conditions to create hybrid models of cyber-physical systems
Hussain, M. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.235043
Case Study: Situational Awareness for Water Treatment Systems
Neshenko, N., Bou‐Harb, E., & Furht, B. (2022) · Springer eBooks · doi:10.1007/978-3-031-18464-2_5
Detection of Cyber-Physical Attacks using Physical Model with Nonparametric EWMA Detector
Supriyadi, J., Istiyanto, J. E., & Putra, A. E. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131057
A Novel Unsupervised Anomaly Detection Approach Using Neural Transformation in Cloud Environment
Zhang, R., Chen, J., Song, Y., Shan, W., Chen, P., & Xia, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23498-9_9
Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization
Zhao, L., Li, B. H., Jia, J., et al. (2022) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc55942.2022.10004139
“Datasets used in this work are derived from Secure Water Treatment (SWaT) [15].”
Method
PENGOLAHAN DAN PENJERNIHAN AIR UNTUK PRODUKSI AIR MINUM DALAM KEMASAN MENGGUNAKAN MESIN FILTER UV
Pulansari, F., Widjajati, E. P., Nugraha, I., et al. (2022) · SELAPARANG Jurnal Pengabdian Masyarakat Berkemajuan · doi:10.31764/jpmb.v6i4.10627
Intrusion Detection System for Industrial Control Systems Based on Imbalanced Data
Dong, X., & Lai, Y. (2023) · IEEE International Symposium on Autonomous Decentralized Systems (ISADS) · doi:10.1109/isads56919.2023.10092008
“LoRAS divides minority class into different neighborhoods, controls the local variance of synthesized samples by generating multiple shadow samples (Gaussian noise of minority class samples) in the neighborhood and randomly selecting shadow samples for a convex combination [10].”
BackgroundMethod
Endüstriyel Kontrol Sistemlerinde Yenilikçi Anomali Tespit Sistemlerinin İncelenmesi
ÇINAR, K., & İskefiyeli, M. (2023) · Mühendislik Bilimleri ve Araştırmaları Dergisi · doi:10.46387/bjesr.1230141
Digital Neural Network Model of the Detector of Anomalous Changes in the Signals Reflecting the Operation of Automated Process Control Systems of Enterprises under the Influence of Cyber Attacks
Glebets, A. L., Golovlev, M. O., & Николаевич, Р. А. (2023) · International Conference on Digital Signal Processing and its Applications (DSPA) · doi:10.1109/dspa57594.2023.10113395
Simple Heuristics as a Viable Alternative to Machine Learning-Based Anomaly Detection in Industrial IoT
Bicski, B., Farkas, K., & Pekár, A. (2023) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2200232
“meth-ods using the Secure Water Treatment (SWaT) dataset [13].”
Method
Identifying Anomalies in Industrial Networks: A Proposed Testbed for Experimental Evaluation
Kuchař, K., & Blažek, P. (2023) · Conference STUDENT EEICT · doi:10.13164/eeict.2023.264
“In paper [11] Jonathan Goh, et al. presented a secure water treatment testbed/dataset.”
Background
Multivariate Time Series Anomaly Detection in a Regularization Perspective
Guo, Q., -, J. Z., Chen, Y., & Liu, R. (2023) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3594315.3594655
“Algorithm 1: ARGAN training algorithm Input: Normal windows Dataset 𝑿 = { 𝑿 (1) , . . . ,𝑿 (𝑇 ) } , Number epochs 𝑁 Output: Trained module 𝑝 , 𝑞, 𝑒 , 𝑟 and 𝑑 1 𝑝, 𝑞, 𝑒, 𝑟, 𝑑 initialize weights; 2 𝑛 ← 1; 3 repeat 4 for 𝑡 = 1 to 𝑇 do 5 𝒛 (𝑡 ) = 𝑞(𝑿 (𝑡 ) ) 6 𝑿 (𝑡 ) = 𝑝 (𝒛 (𝑡 ) ) 7 𝑿 (𝑡+1) = 𝑟 (𝒛 (𝑡 ) ) 8 ?”
BackgroundInfluential
Monitoring the State of Robotic Systems Based on Time Series Analysis
Semenov, V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43111-1_24
Unsupervised Attack Isolation in Cyber-physical Systems: A Competitive Test of Clustering Algorithms
Su, K., Ahmed, C. M., & Zhou, J. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-30191-9_3
Attack Model for the Industrial Water Treatment Systems
Владимировна, Ф. Е., Novikova, E. S., & Saenko, I. B. (2023) · International Conference on Control in Technical Systems (CTS) · doi:10.1109/cts59431.2023.10288930
“…of the related research shown, that there are the following security related water treatment datasets: a Secure Water Treatment (SWaT) dataset from the University of Technology and Design, Singapore [8], Water Distribution (WADI) dataset [9], and HIL-based augmented ICS (HAI) security dataset [10].”
Background
Exploring the i3DVAE-LSTM Framework for Generating Exceptionally Rare Anomaly Signals
Kaewkiriya, T., & Woraratpanya, K. (2023) · doi:10.1109/icitee59582.2023.10317760
“To assess the effectiveness of the proposed framework, the researchers generated anomalous signals using real-world datasets [13], [14].”
Method
LSCR: Latent Space Coordination Relation for Anomaly Prediction
Chalongvorachai, T., & Woraratpanya, K. (2023) · doi:10.1109/icitee59582.2023.10317782
“To evaluate the effectiveness of the proposed framework, the Secure Water Treatment (SWaT) datasets from iTrust [1] were utilized in the experiments conducted for this paper.”
BackgroundMethod
Segmentation-Based Adversarial Denoising Auto-Encoder for Anomaly Detection in Multivariate Time Series Data
Zhang, K., Li, C., Yang, Q., Lin, W., & Yuan, L. (2023) · International Conference on Information Science and Technology · doi:10.1109/icist59754.2023.10367117
“4) Secure Water Treatment (SWT) Dataset: This dataset [25] represents a scaled-down version of a real-world industrial water treatment plant.”
Background
Translated Temporal Component-Enhanced Attention for Multivariate Time-Series Anomaly DetectionPreprint
Seo, M., & Kim, M. H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4743831
MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical Systems with Multi-Process Generative Adversarial Networks*
Zhou, Y., Wang, J., Tang, J., Gou, C., Jiang, Z., Li, D., & Ng, S.-K. (2023) · International Conference on Artificial Intelligence of Things and Systems (AIoTSys) · doi:10.1109/aiotsys58602.2023.00049
“The ﬁrst 21600 samples were removed from the training data since the included physical system did not reach steady states during the startup phase [28].”
Method
An Attack Prediction and Recognition Method for Water Treatment System Based on One-dimensional Convolutional Neural Network and Cumulative Sum
Hu, X., & Liu, L. (2024) · doi:10.1109/nnice61279.2024.10498833
“The SWaT dataset adopted in this paper was developed by iTrust Network Security Research Center of Singapore University of Technology and Design [10].”
Method
A Multi-scale Parallel Unsupervised Model for Multivariate Time Series Anomaly Detection
Bao, J., Gao, H., Zhang, C., Jia, W., Gao, J., & Yang, T. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63223-5_18
A Data-Driven Approach to Intrusion Detection for Industrial Control Systems
Imanpourgoorabzarmakhi, M., Arunmozhi, M., Al-Khateed, H., & Kumar, P. M. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-92-3681-7_19
A Collaborative System for Machine Learning-Based Final-Year Projects With Enhanced Dataset Accessibility
Lounas, R., Djerbi, R., Mokrani, H., et al. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-0683-3.ch019
Analysis of CNN-LSTM Model Performance Under Targeted Adversarial Attacks in Water Treatment System
Kara, E., Ayas, M. Ş., & Ayas, S. (2024) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp63128.2024.10605932
“The SWaT testbed, which was developed at the Singapore University of Technology and Design, is a small-scale water treatment system [ 11].”
Method
A Simulation Method for Industrial Control Honeynet Based on Time Series Prediction
Jiang, T., Yu, Y., Xu, C., et al. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667509
“At the same time, in order to improve the interaction ability of the industrial control honeypot, this article modifies the configuration of the Confot honeypot based on the relevant data of the SWaT [10] dataset by adding real device status information and supporting function codes.”
Method
Sensor Embedding and Variant Transformer Graph Networks for Multi-source Data Anomaly Detection
Ma, L., Huang, Z., Peng, B., Zhang, M., He, W., & Wang, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-7001-4_27
HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control Systems Using MLPNN and Attention Mechanisms
Abinash, S., Sriram, V. S. S., Krithivasan, K., & Priyanga, S. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9743-1_13
Certainly Uncertain: Demystifying ML Uncertainty for Active Learning in Network Monitoring Tasks
Dietz, K., Hajizadeh, M., Wehner, N., Geißler, S., Casas, P., Seufert, M., & Hoßfeld, T. (2024) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm62983.2024.10814433
Time Series Anomaly Detection Based on Normalized Flow and Bayesian Networks
Yao, H., Liu, D., Chang, Y., et al. (2024) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc63484.2024.00077
“• SWaT: We also use the public Safe Water Treatment (SWaT) dataset from the Singapore Public Utilities Commission [15] for evaluation.”
Hardware-Friendly Nyström Approximation for Water Treatment Anomaly Detection
Aftowicz, M., Fritscher, M., Lehniger, K., et al. (2024) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon55916.2024.10905880
“In this work we used the OCSVM algorithm for the detection of anomalies in a water treatment process, based on the Secure Water Treatment (SWaT) dataset [5].”
Influential
Dynamic Soft Contrastive Learning for Time Series Anomaly Detection
Song, Y., Liu, Y., & Shu, S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889377
“We evaluate our method on four public datasets: Secure Water Treatment (SWaT) [19], Mars Science Laboratory rover (MSL) [20], Water Distribution (WADI) [21], and Pooled Server Metrics (PSM) [22].”
Towards Securing Water Systems Communications from Adversarial Attacks using AI Assurance
Yardimci, M. O., & Batarseh, F. A. (2025) · IEEE Middle East and North Africa Communications Conference (MENACOMM) · doi:10.1109/menacomm62946.2025.10911020
“Main WDS processes and the locations of the sensors selected [19] In this study, the LSTM model is first used to learn the normal patterns from the training data, and then the test data are used to evaluate model performance on the test data.”

Authors: Goh, J., Adepu, S., Junejo, K. N., & Mathur, A.
Affiliations: Singapore University of Technology and Design (SG)
Corresponding author: Goh, Jonathan
ORCID: Adepu, Sridhar
Junejo, Khurum Nazir
Mathur, Aditya
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2017
Pages: 88-99
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-319-71368-7_8
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
Licence: www.springer.com/tdm (unspecified)
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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A Dataset to Support Research in the Design of Secure Water Treatment Systems

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