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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Whitelisting for Characterizing and Monitoring Process Control Communication
Paul, A., Schuster, F., & König, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39828-5_2 · Cited by 4
Retrofitting Integrity Protection into Unused Header Fields of Legacy Industrial Protocols
Wagner, E., Rothaug, N., Wolsing, K., Bader, L., Wehrle, K., & Henze, M. (2023) · Conference on Local Computer Networks · doi:10.1109/lcn58197.2023.10223384 · Cited by 4
“However, even the decently sizes SWAT testbed [17] requires less than 200 session ids over an one hour period.”
Method
AICrit: A Design-Enhanced Anomaly Detector and Its Performance Assessment in a Water Treatment Plant
Raman, M. R. G., & Mathur, A. P. (2023) · Applied Sciences · doi:10.3390/app132413124 · Cited by 4
Self-citation
Sketching Multidimensional Time Series for Fast Discord Mining
Yeh, C. M., Zheng, Y. L., Pan, M., et al. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386363 · Cited by 4
“SWaT dataset contains the measurement of a scaled-down version of a real-world industrial water treatment plant under the normal and attacked scenarios [ ?”
BackgroundMethod
Design and Realization of Testbeds for Security Research in the Industrial Internet of Things
Tippenhauer, N. O. (2019) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-12330-7_14 · Cited by 4
Detecting Attacks on a Water Treatment System Using Oneclass Support Vector Machines
Yau, K., Chow, K.-P., & Yiu, S. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-56223-6_6 · Cited by 4
“The attacks fell in the following four categories [7]:”
Detection of cyber-attacks in systems with distributed control based on support vector regression
Nedeljkovic, D. M., Jakovljević, Ž., Miljković, Z., et al. (2020) · Telfor Journal · doi:10.5937/telfor2002104n · Cited by 4
“Detected attacks on the LIT301; the details regarding the attacks can be found in [16].”
BackgroundInfluential
A Prediction-Augmented AutoEncoder for Multivariate Time Series Anomaly Detection
Gong, S., Wu, Z., Liu, Y., Lin, Y., & Wang, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92185-9_56 · Cited by 4
A Deep Neural Network for Anomaly Detection and Forecasting for Multivariate Time Series in Smart City
He, J., Dong, M., Bi, S., Zhao, W., & Liao, X. (2019) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber46603.2019.9066655 · Cited by 4
Applying of Recurrent Neural Networks for Industrial Processes Anomaly Detection
Alabugin, S. K., & Sokolov, A. N. (2021) · Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) · doi:10.1109/usbereit51232.2021.9455060 · Cited by 4
“To conduct the experiments, the Secure Water Treatment (SWaT) dataset [12] was used.”
Method
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems
McGuan, C., Yu, C., & Lin, Q. (2023) · 2023 IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi58743.2023.10297207 · Cited by 3
“In recent years, physical testbeds have been made available such as SWaT [3] (water treatment system), WADI [8] (water distribution system), EPIC [9] (electric power system), etc.”
Method
Security in Process: Visually Supported Triage Analysis in Industrial Process Data
Lohfink, A., Antón, S. D. D., Schotten, H. D., et al. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.2969007 · Cited by 3
“A detailed list including a description can be found in [13].”
Background
VG-Net: Sensor Time Series Anomaly Detection with Joint Variational Autoencoder and Graph Neural Network
Wang, S., Liu, S., Fan, X., Wang, H., Zhou, Y., Gao, H., Lu, H., & Deng, X. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00094 · Cited by 3
“The SWaT [25] (Secure Water Treatment) and WADI [26] (Water Distribution) datasets were created and made available by the iTrust Cybersecurity Research Centre at the Singapore University of Technology and Design.”
Boundary-aware adversarial ensemble learning for multivariate time series anomaly detection
He, P., Tan, X., & Cheng, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114168 · Cited by 4
Adversarial Data Augmentation Enhanced LSTM Autoencoder for Anomaly Detection in Industrial Pipeline Networks
Chen, X., Yu, F., Lin, X., et al. (2025) · Journal of Signal Processing Systems · doi:10.1007/s11265-025-01962-x · Cited by 3
“This indi-Figure Six stages of the SWaT dataset [11]. cates whether the system was operating under typical conditions or if an attack or anomaly was detected.”
Anomaly Detection in Industrial Internet of Things: A Graph Learning Approach With Spatiotemporal Interactions
Feng, N., Li, J., Zhao, H., Zhao, L., & Li, M. (2026) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2026.3657744 · Cited by 4
“However, these datasets often lack detailed records on how the anomaly spreads, which nodes are affected, and the duration of the impact [15], [25].”
Enabling Joint Benchmarking of Automated Root Cause Analysis and Causal Discovery in Manufacturing Using the causRCA Dataset
Mehling, C. W., Pieper, S., Lüke, T., et al. (2026) · Procedia CIRP · doi:10.1016/j.procir.2025.09.010 · Cited by 3
Automated Anomaly Detection Tool for Industrial Control System
Varkey, M., John, J., & Umadevi, K. S. (2022) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc54232.2022.9888891 · Cited by 3
“, the water produced by the RO unit cleans the membranes [19] [20].”
Background
Deep Learning-Based Anomaly Detection for Time-Series Data in Industrial Control Systems
Zhang, Y. A., Li, B., & Zhang, X. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10241095 · Cited by 3
Multimodal Game-Theoretic Cyber-Attack Projection in Industrial Control Systems
Jahromi, A. N., Karimipour, H., Halabi, T., Zhu, Y., & Gadekallu, T. R. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3433565 · Cited by 3
“…despite the other related studies that consider one attack scenario per model, 38 different attack scenarios are deﬁned for the environment based on the cyber-attacks in [48] and recent cyber-attacks against water treatment facilities [9], to make sure that they are aligned with the real-world.”
Adversarial Attack against Intrusion Detectors in Cyber-Physical Systems With Minimal Perturbations
Bai, M., Liu, P., Lv, F., Fang, D., Lv, S., Zhang, W., & Sun, L. (2024) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa63168.2024.00109 · Cited by 5
“…pixels (e.g. a 224 × 224 example in ImageNet [16] dataset contains 50176 pixels) for distributing subtle perturbations, the tabular data used by IDS, such as the SWaT dataset [17] with 51 attributes and the Water Distribution Testbed dataset [18] with 41 attributes, has far fewer attributes.”
Sensor Fusion Enhances Anomaly Detection in a Flood Forecasting System
Ma, A., Karande, A., Dahlquist, N., Ferrero, F., & Nguyen, N. R. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14020034 · Cited by 3
“This is similar to how the SWaT dataset includes two separate data streams, whether the testbed is running in either a normal state or attacked state [38].”
SRT: A Skip-Range Transformer for Detecting Anomalies in Multiattribute Industrial Time Series Data
Gao, H., Jiang, W., Ran, Q., Wang, K., Ma, X., & Xu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3565669 · Cited by 3
“The SWaT [26] dataset, which was designed to support the development of secure water treatment systems, originated from a six-stage testing platform simulating an operational industrial plant.”
KeAD: Knowledge-enhanced Graph Attention Network for Accurate Anomaly Detection
Li, Y., Zhou, Z., Sun, P., Deng, S., Sun, X., Xue, X., Yangui, S., & Gaaloul, W. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3583241 · Cited by 3
“Speciﬁcally, we eliminated the ﬁrst 21,600 samples of each dataset, considering the fact that it usually takes 5-6 hours to reach stabilization when ﬁrst started [36].”
An approach to the identification of the state of elements in cyber-physical systems based on principal component analysis
Semenov, V. V. (2021) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2021-21-6-887-894 · Cited by 3
System Identification Methods for Industrial Control Systems
Hussain, M., Fidge, C., Foo, E., & Jadidi, Z. (2022) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-031-08270-2_2 · Cited by 3
Neutralizing Adversarial Machine Learning in Industrial Control Systems Using Blockchain
Moradpoor, N., Barati, M., Robles-Durazno, A., et al. (2023) · Springer proceedings in complexity · doi:10.1007/978-981-19-6414-5_24 · Cited by 3
Detecting Anomalies in Industrial Control Systems with LSTM Neural Networks and UEBA
Piñón-Blanco, C., Otero-Vázquez, F., Ortega-Fernandez, I., & Sestelo, M. (2023) · doi:10.23919/jnic58574.2023.10205609 · Cited by 4
“We have identified established datasets commonly used in the literature, namely Secure Water Treatment [39] and Water Distribution (WADI) [40].”
Method
A cross-layered cluster embedding learning network with regularization for multivariate time series anomaly detection
Long, J., Luo, C., Chen, R., Yu, J., & Li, K. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05833-9 · Cited by 3
Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding
Nivarthi, C. P., & Sick, B. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00218 · Cited by 4
“Our research utilized the Secure Water Treatment (SWaT) open-source dataset [19], comprising 11 days of data from a water treatment plant with 51 sensors and actuators.”
Method
Curriculum Learning-Based Multivariate Time Series Anomaly Detection
Dube, D. C., & Akar, M. (2024) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct61870.2024.10516396 · Cited by 3
“The duration of the attacks vary from about one minute to an hour [32], [33].”
Background
Fault Detection and Isolation of Cyber-Physical System under Cyber-Attack Using Bayesian Network
Tanwar, M., Raghavan, N., & Dammala, P. K. (2024) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm61352.2024.10627322 · Cited by 3
“The attack and physical failure probabilities are estimated based on cyber-attack data [18].”
BackgroundMethodInfluential
MuLDOM: Forecasting Multivariate Anomalies on Edge Devices in IIoT Using Multibranch LSTM and Differential Overfitting Mitigation Model
Li, P., Wu, M., Zhang, Y., Xia, J., & Wang, Q. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3448505 · Cited by 4
“1) Process Miner’s Rare Event Detection Data Set: PMRED is a real data set collected from industrial sensor equipment in an intelligent paper mill, used for predicting rare anomaly events in multivariate time series of industrial equipment.”
Autonomic Passive IT-OT Device Classification in ICS/SCADA Networks
Rubin, B., Tekeoglu, A., & Rouff, C. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679418 · Cited by 3
Analysis of Historical Data Usage in Ensemble of Classification Methods within a Two-Stage Hybrid Model of ICS Subjected to Cyberattacks
Bukharev, D. A., & Sokolov, A. N. (2024) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon61949.2024.10694329 · Cited by 3
“The dataset used for this study, employing hierarchical clustering analysis methods, is the Secure Water Treatment (SWaT) dataset [18].”
Method
Bridging the Gap: A Comparative Analysis of ICS and IT Datasets for IDS Evaluation
Koneru, S. S., & Cho, J. (2024) · doi:10.1109/fllm63129.2024.10852471 · Cited by 3
IIoT’s Risk Odyssey: Navigating the Risk Propagation of Illegal Information Flows
Anagnostopoulou, A., Mavridis, I. K., Athanasopoulos, M., Mylonas, A., & Gritzalis, D. A. (2025) · IEEE Access · doi:10.1109/access.2025.3555873 · Cited by 3
“Public datasets such as BATADAL [36], SWaT [37], and WADI [38] primarily concentrate on SCADA systems within power grids and fail to provide specific features related to power system measurements, protocols, or new smart devices like smart inverters, IEDs, and communication infrastructure.”
Comparison of Design-Centric and Data-Centric Methods for Distributed Attack Detection in Cyber-Physical Systems
Leopold, J. L., McMillin, B., Stiffler, R., et al. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-62840-6_13 · Cited by 3
Toward A Sustainable Cyber-Physical System Architecture for Urban Water Supply System
Wu, D., Wang, H. H., & Seidu, R. (2020) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00089 · Cited by 3
“For water treatment, Goh J. et al [30] published a dataset with 11 day recordings in order to defend for possible attacks.”
TCMD: A Two-Tier Classification Model for Anomaly-Based Detection in IoT
Alghanmi, N., Alotaibi, R., & Buhari, S. M. (2019) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds.2019.00013 · Cited by 3
“[22] applied unsupervised learning Deep Neural Networks (DNNs) and OC-SVM to the Secure Water Treatment (SWaT) dataset [23, 24].”
Method
Industrial Control System Intrusion Detection Model based on LSTM & Attack Tree
Xingjie, F., Guogenp, W., ShiBIN, Z., & ChenHAO (2020) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip51612.2020.9317477 · Cited by 4
“This paper uses SWaT [13] for simulation experiment.”
Method
DINI: data imputation using neural inversion for edge applications
Tuli, S., & Jha, N. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-24369-1 · Cited by 3
“The second is the SWaT dataset 19 with a diverse set of categorical and continuous input features, and detection of attack as the prediction label.”
Background
IoT Sensor Data Consistency using Deep Learning
Zualkernan, I. A., Ahmed, N., Elmeligy, A. O., et al. (2022) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais56727.2022.9975955 · Cited by 3
“The model was trained and tested on two complex unlabeled cyber-attack datasets namely SWaT [10] and WADI [11].”
MethodInfluential
ADGAS: An Advanced Data Generation for Anomalous Signals
Chalongvorachai, T., & Woraratpanya, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-79757-7_14 · Cited by 3
Security in Process: Detecting Attacks in Industrial Process DataPreprint
Antón, S. D. D., Lohfink, A. P., Garth, C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03730 · Cited by 3
“PLCs in this figure correspond to the sub-processes with the respective number. In total, they control 51 sensors and actuators. An exhaustive list including description canbefoundintheworkofGohetal.[14].Theydistinguishbetween four types of attacks, describing the type and listing the number of occurrences in the data…”
BackgroundMethod
Application of Deep Learning on Cyber-Attack Detection and Identification in Industrial Control Systems
Al-Abassi, A. (2020) · The Atrium (University of Guelph) · Cited by 3
Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning
Tang, S., Ding, Y., & Wang, H. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2024.059143 · Cited by 3
A Review of Intrusion Detection Research Based on Deep Learning
Deng, M., Kan, Y., Xu, H., et al. (2023) · International Conference on Signal Processing, Computer Networks and Communications (SPCNC) · doi:10.1145/3654446.3654452 · Cited by 2
“…respectively GAN [20] GAN+CNN NSL-KDD, UNSW-NB15 The accuracy is 93.2%,87% on NSL-KDD, UNSW-NB15 datasets respectively [21] B-GAN+LSTM SWaT [29] Acc:90.97% GNN [22] E-GraphSAGE BoT-IoT [30], TON-IOT [31] The accuracy is 99.99%,97.87% on BoT-IoT, TON-IOT datasets respectively [24]…”
BackgroundInfluential
Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water Utilities
Abisheg, S., Raman, M. R. G., & Mathur, A. P. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9743-1_16 · Cited by 2
Self-citation
Warping resilient robust anomaly detection for multivariate time series
Abilasha, S., & Bhadra, S. (2025) · Machine Learning · doi:10.1007/s10994-024-06689-7 · Cited by 3
“We have done an extensive set of experiments with two widely used CPS (Cyber-Physical Systems) datasets namely Secure Water Treatment (SWaT) (Goh et al., 2017) and Water Distribution (WADI) (Ahmed et al., 2017) and two newly released datasets which are Hardware-In-the-Loop-based Augmented…”
Cybersecurity Analysis of Modern PLCs: Exploring Data Logging and Performance Metrics Using the AXC F 2152
Kaur, A. (2025) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr62650.2025.11020265 · Cited by 2
GPT4EAD: Efficient Large Language Models for Time Series Anomaly Detection on Memory-Constrained Edge Devices
Zhou, Q., Jin, Z., Wu, M., & He, S. (2025) · IEEE Internet of Things Magazine · doi:10.1109/miot.2025.3576029 · Cited by 2
“To demonstrate the effectiveness of our lightweight strategy, we conduct extensive experiments on ﬁ ve public datasets for anomaly detection, i.e., SWAT [13], SMD [7], SMAP, MSL [14], and PSM [15].”
Enhancing Network Security Management in Water Systems using FM-based Attack Attribution
Avdalović, A., Khoury, J. D., Taha, A. F., & Bou‐Harb, E. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073721 · Cited by 2
“This ML-based framework has been shown to effectively detect anomalies across various types of water systems [14], [20], and is applicable across different model architectures [21], [22].”
A deep learning-based adaptive cyber disaster management framework
Neshenko, N., Bou‐Harb, E., Furht, B., & Baghersad, M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01241-3 · Cited by 3
“The SwaT dataset [79] contains data that was automatically received by a historian from all ICS assets every 1 s.”
Deep Multimanifold Transformation-Based Multivariate Time Series Fault Detection
Liu, H., Qiu, X., Shi, Y., Xu, M., Zang, Z., & Lei, Z. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3584988 · Cited by 4
“SWaT [11] and WADI [2] are collected from water treatment and distribution testbeds, simulating industrial cyberattacks with labeled anomalies.”
Machine Learning Approaches for Anomaly Detection in IoT Networks: A Survey
Lim, K., & Prum, S. (2025) · doi:10.1109/skima66621.2025.11155642 · Cited by 2
Adversarial Reconstruction with Spectral-Augmented and Graph Joint Embedding for Network Anomaly Detection
Yu, L., Wu, J., Chen, Q., et al. (2025) · Electronics · doi:10.3390/electronics14183730 · Cited by 2
RDSAD: Robust Threat Detection in Evolving Data Streams via Adaptive Latent Dynamics
Alsaedi, A., Tari, Z., & Mahmud, R. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3614294 · Cited by 2
“Four benchmark datasets, well-established in security research, are used for the experiments: TON_IoT [44], Secure Water Treatment (SWaT) [45], Water Distribution Dataset (WADI) [46], and GAS [47].”
Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review
Sayyaf, M. I., Pascacio, P., Zhu, N., & Renaudin, V. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3616395 · Cited by 5
“ICS/IoT demonstrates strong adoption 74%, utilizing datasets such as SWaT [24] and WADI [1], while medical/health shows comparable adoption 75%.”
Anomaly Detection and Objective Security Evaluation Using Autoencoder, Isolation Forest, and Multi-Criteria Decision Methods
Zhang, H., & Zhang, H. (2025) · Sensors · doi:10.3390/s25196250 · Cited by 5
The HAI-CPPS Benchmark: Evaluating AI Capabilities across Hybrid Data Spaces
Moddemann, L., Ehrhardt, J., Diedrich, A., & Niggemann, O. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205680 · Cited by 2
“The SWAT [18] and WADI [19] datasets are collected from a real-world water treatment and distribution testbed, and serve as benchmarks for evaluating anomaly detection for CPPS security.”
Network-Based Real-Time Detection of Data Manipulation Attacks in Industrial Control Systems
Raman, M. R. G., Khandekar, S., Murarishetti, R., Caven, C. Z. Y., Eric, N. G. F., & Zhou, J. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01172-3 · Cited by 3
“For detailed architectural information on the IT and OT network structure of SWaT, readers are referred to[33].”
Guardians of ICS: A Comparative Analysis of Anomaly Detection Techniques
Wang, Z., Umer, M., Zhang, H., Hassan, N. U., & Ahmed, C. M. (2026) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/cps2.70037 · Cited by 2
Adaptive Latent Distribution Modeling for Industrial Time Series Anomaly Detection
Liu, Y., Song, Y., Shu, S., Lin, F., Wang, J., & Guo, Y. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3674236 · Cited by 2
Physics-guided contrastive temporal graph learning for anomaly detection and root-cause localization in industrial control systems
Rajalakshmi, M., & Velmurugan, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-45677-w · Cited by 2
“Huang et al. looked at false data injection (FDI) attack detection using both time-and frequency-domain analysis of sensor data, and employed deep learning models to learn discriminative features from these domains 40 .”
ADVIS-G: An Adversarially Defended Intrusion Detection System for Smart Grids Using Deep Learning
Acharya, R., Sardy, L. A., Muhammad, M., & German, R. (2026) · Künstliche Intell. · doi:10.1007/s13218-026-00905-3 · Cited by 3
Reverse Engineering of Industrial Protocols From Network Traffic
Sheng, C., Jiang, S., Han, Q.-L., et al. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3681937 · Cited by 2
“Bycontrast,datasets,such asSWaT[100]andWADI[101],provideprocess-awareevi-dence,includingnetworktrafﬁc,sensor/actuatorreadings,and operationallogscollectedoversustainedplantexecution,and arethereforemorerelevanttopayloadsemanticinferenceand behavior-orientedanalysis.”
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
Qi, Z., Zhang, J., Li, W. X., & Liang, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0372-7_38 · Cited by 2
EvoPhySec: A Multi-Objective Evolutionary Framework for Adaptive Calibration of Physics-Informed Anomaly Detection in Smart City IoT Networks
Radovanović, D., Savanović, N., Janaćković, J., et al. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090285 · Cited by 2
Teaching Advanced AI Development Techniques with a New Master’s Program in Artificial Intelligence Engineering
Созыкин, А. В., Kuklin, E., & Iumanova, I. F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22941-1_41 · Cited by 2
Causanom: Anomaly Detection With Flexible Causal Graphs
Strelnikoff, S., Jammalamadaka, A., & Lu, T.-C. (2023) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.36.133298 · Cited by 3
“Since (Goh et al., 2017) noted that the systems only reach stabilization after at most 6 hours, these initial samples are removed from both datasets.”
Background
SWaP: A Water Process Testbed for ICS Security Research
Calder, M., Ahmed, C. M., & Nagaraja, S. (2023) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins57856.2023.10189315 · Cited by 2
“In [5] they perform a comprehensive series of attacks against all stages in their testbed and publish their dataset.”
Background
AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection
[Olga Tushkanova, E. A. ]., Bergadano, F., & Giacinto, G. (2023) · MDPI eBooks · doi:10.3390/books978-3-0365-8265-8 · Cited by 2
UMAD-G: Unsupervised Multi-Modal Time Series Anomaly Detection via Graph
Lee, J., & Baek, J. (2024) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463361 · Cited by 2
“Secure Water Treatment (SWaT) dataset [7] originates from a Testbed that is a scaled-down version of a real water treatment plant.”
Background
Anomaly Detection in Multivariate Time Series with Contaminated Training Data Using VAE
Lv, D., Ma, M., Dong, H., Shang, Y., & Bao, K. (2024) · IEEE Conference on Big Data Security on Cloud (BigDataSecurity) · doi:10.1109/bigdatasecurity62737.2024.00010 · Cited by 3
“Our algorithm’s performance is assessed using the Secure Water Treatment (SWaT) dataset, sourced from a real-world operational test-bed for water treatment [36].”
Method
Anomaly Signal Imputation Using Latent Coordination Relations
Chalongvorachai, T., & Woraratpanya, K. (2024) · IEEE Access · doi:10.1109/access.2024.3448236 · Cited by 2
“Specifically, the Water Treatment (SWaT) and Water Distribution (WADI) datasets [18], [19] were employed, as elaborated in Section IV-B.”
Method
Detecting Cyber Attacks in Industrial Control Systems Using Duration-aware Representation Learning
Lan, B., & Yu, S. (2024) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3669754.3669812 · Cited by 2
“The Secure Water Treatment (SWaT) testbed which is the most representative dataset in the field of ICSs was used in our evaluation [29].”
Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network
Shan, J., Cai, D., Fang, F., Khan, Z., & Fan, P. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3511951 · Cited by 4
“Our experiments will compare baseline models with our proposed model on these datasets, which are introduced as • Secure Water Treatment (SWaT) dataset 2 : This IIoT dataset spans 11 days and includes related to water treatment [36].”
Enhancing Anomaly Detection in Multivariate Time Series with Stacked Transformer Encoders and Adaptive Positional Embeddings
Sowmya, K., & Ramesh, K. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09821-w · Cited by 4
“Secure Water Treatment (SWaT) Dataset The SWaT dataset [44], derived from a cybersecurity-focused water treatment testbed, was selected to test our model’s performance in critical infrastructure scenarios.”
Toward Identifying Cyber Dependencies in Water Distribution Systems Using Causal AI
Sobien, D., Kulkarni, A., & Batarseh, F. A. (2024) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-6488 · Cited by 2
Integrating Self-Organizing Map and Graph Neural Networks to Detect Anomalies in Time-Series Data
Su, P., Yuan, H., Lu, Z., & Chen, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3509632 · Cited by 2
“We use three public datasets SKAB [34], iTrsut-SWAT [35], and BATADAL [36] to assess the proposed framework.”
Enhancing Industrial Control Systems Security: Real-Time Anomaly Detection with Uncertainty Estimation
Birihanu, E., Soullami, A., & Lendák, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78980-9_7 · Cited by 2
Hybrid Unsupervised Time-Series Anomaly Detection for Industrial IoT Based on Spatio-Temporal Feature
Yu, D., Yao, M., Qi, P., & tao, D. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2189-7_9 · Cited by 2
Forecasting Error Pattern-Based Anomaly Detection in Multivariate Time Series
Park, S., Han, S., & Woo, S. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67667-4_10 · Cited by 2
DEVELOPMENT OF MODELS AND METHODS FOR EARLY DETECTION OF CYBER ATTACKS ON ENERGY FACILITIES OF A METALLURGICAL ENTERPRISE
Sokolov, A. N., Николаевич, Р. А., Barinov, A. E., et al. (2021) · Journal of the Ural Federal district Information security · doi:10.14529/secur210308 · Cited by 2
Cost-Effective Detection of Cyber Physical System Attacks
Sarmah, U., & Bhattacharyya, D. K. (2022) · Intelligent systems reference library · doi:10.1007/978-981-16-8930-7_2 · Cited by 2
3DVAE-LSTM for Extremely Rare Anomaly Signal Generation
Kaewkiriya, T., & Woraratpanya, K. (2022) · doi:10.1109/icitee56407.2022.9954112 · Cited by 2
“For performance evaluation, we generated anomalous signals by the proposed framework with real-world datasets [14], [15].”
MethodResult
An error neighborhood-based detection mechanism to improve the performance of anomaly detection in industrial control systems
Shen, W., & Yang, G. (2022) · International Conference on Mechanical, Automation and Electrical Engineering (CMAEE) · doi:10.1109/cmaee58250.2022.00013 · Cited by 2
“2, the anomalies caused by smart cyberattacks are continuous and have a similar trend with normal behavior in the Secure Water Treatment (SWAT) system [7].”
BackgroundInfluential
Comparative Analysis of the Clustering Methods Application for Detecting Anomalies in the Information Processes of ICS Networks Exposed to Cyberattacks
Bukharev, D. A., Николаевич, Р. А., & Sokolov, A. N. (2023) · IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) · doi:10.1109/usbereit58508.2023.10158832 · Cited by 2
“Hierarchical cluster analysis techniques were employed in the study, using the Secure Water Treatment (SWaT) dataset [16].”
Method
USING MACHINE LEARNING METHODS FOR INDUSTRIAL CONTROL SYSTEMS INTRUSION DETECTION
Shikhaliyev, R. (2023) · Problems of Information Technology · doi:10.25045/jpit.v14.i2.05 · Cited by 2
Traceable Transformer-Based Anomaly Detection for a Water Treatment System
Qin, S., Lang, Y., & Chow, K.-P. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-42991-0_12 · Cited by 2
SECURITY OF CYBER-PHYSICAL SYSTEMS DATA COLLECTION METHODOLOGY FOR SECURITY ANALYSIS OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS
(2023) · Voprosy kiberbezopasnosti · doi:10.21681/2311-3456-2023-5-69-79 · Cited by 2
Matrix Profile XXIX: C22MP, Fusing catch 22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector
Tafazoli, S., Lu, Y., Wu, R., et al. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00066 · Cited by 2
GAN-Based Anomaly Intrusion Detection for Industrial Controller System
Raj, R. V. R., Sangeetha, V., & Amritha, P. P. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-8346-9_7 · Cited by 2
ObjSim: efficient testing of cyber-physical systems
Sun, J., & Yang, Z. (2020) · doi:10.1145/3402842.3407158 · Cited by 2
“an established expert-crafted benchmark [5].”
Background
Assessment of the Applicability of Autoencoders in the Problem of Detecting Anomalies in the Work of Industrial Control Systems
Pyatnisky, I. A., & Sokolov, A. N. (2020) · Global Smart Industry Conference (GloSIC) · doi:10.1109/glosic50886.2020.9267864 · Cited by 2
“The Secure Water Treatment (SWaT) dataset [16] was used to conduct experimental research.”
Method
Bank of Models: Sensor Attack Detection and Isolation in Industrial Control Systems
Ahmed, C. M., & Zhou, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93200-8_1 · Cited by 2
Cryptographic Solutions for Cyber-Physical System Security
Jin, C. (2019) · OpenCommons at University of Connecticut (University of Connecticut) · Cited by 2
FALCON: Framework for Anomaly Detection In Industrial Control Systems
Sapkota, S. (2019) · doi:10.18122/td/1638/boisestate · Cited by 2
“They utilize labeled attack and normal data from SWaT test bed [22] to train classification models using various ML algorithms.”
BackgroundMethodInfluential
Cyber Situational Awareness Frontiers
Neshenko, N., Bou‐Harb, E., & Furht, B. (2022) · Springer eBooks · doi:10.1007/978-3-031-18464-2_3 · Cited by 1

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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