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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
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An Investigation into the Response of a Water Treatment System to Cyber Attacks
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Items 201–300 of 734
Madhawa, S., Balakrishnan, P., & Umamakeswari, A. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169670 · Cited by 17
“Real-time multivariate dataset [11] comprising both normal and attack data affecting the payload from a water processing facility acquired from iTrust, a research hub is actualized in this effort.”
Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems
Liu, L., Hu, M., Kang, C., et al. (2020) · Information · doi:10.3390/info11020105 · Cited by 17
“SWaT (The Secure Water Treatment) is a water treatment testbed for network security research [22].”
BackgroundLamshöft, K., Neubert, T., Kraetzer, C., et al. (2021) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3437880.3460413 · Cited by 17
“For the experimental evaluation we use the SecureWater Treatment (SWAT) dataset from iTrust [6] as it provides a large amount of real process data, which allows for a realistic simulation of our attack”
BackgroundMethodAICrit: A unified framework for real-time anomaly detection in water treatment plants
Raman, M. R. G., & Mathur, A. P. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.103046 · Cited by 16
Self-citationIndustrial Control Systems Security via Runtime Enforcement
Ruggero Lanotte, A., Merro, M., & Munteanu, A. (2022) · ACM Transactions on Privacy and Security · doi:10.1145/3546579 · Cited by 16
“The SWaT system has been used to provide a dataset containing physical and network data recorded during 11 days of activity [27].”
BackgroundMethodCan Industrial Intrusion Detection Be SIMPLE?
Wolsing, K., Thiemt, L., van Sloun, C., Wagner, E., Wehrle, K., & Henze, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17143-7_28 · Cited by 18
“We evaluate our IIDSs on four state-of-the-art industrial datasets based on physical testbeds and including attacks against the industrial process: SWaT [38], the most widely-used dataset, represents a multi-staged water treat-ment system.”
MethodAssessing Machine Learning Techniques for Intrusion Detection in Cyber-Physical Systems
dos Santos, V. F., Albuquerque, C. V. N., Passos, D. G., et al. (2023) · Energies · doi:10.3390/en16166058 · Cited by 16
Edge conditional node update graph neural network for multivariate time series anomaly detection
Jo, H., & Lee, S. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121062 · Cited by 16
Sparse Non-Linear Vector Autoregressive Networks for Multivariate Time Series Anomaly Detection
Belay, M. A., Rasheed, A., & Rossi, P. S. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3520019 · Cited by 16
“E XPERIMENTAL S ETUP We conducted our experiments using two publicly available MTS datasets: Secure Water Treatment (SWAT) [21], [22] and Water Distribution (WADI) [23].”
A behavioral-based forensic investigation approach for analyzing attacks on water plants using GANs
Neshenko, N., Bou‐Harb, E., & Furht, B. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301198 · Cited by 16
Gulzar, Q., & Mustafa, K. (2024) · Security and Privacy · doi:10.1002/spy2.440 · Cited by 19
Wang, K., Kong, J., Zhang, M., Jiang, M., & Liu, T. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129887 · Cited by 16
Moezzi, S. A., Rastgar, S., Faghani, M., Ghiasvand, Z., & Khoei, A. J. (2025) · Chemosphere · doi:10.1016/j.chemosphere.2025.144304 · Cited by 17
A Lightweight Unsupervised Intrusion Detection Model Based on Variational Auto-Encoder
Ren, Y., Feng, K., Hu, F., Chen, L., & Chen, Y. (2023) · Sensors · doi:10.3390/s23208407 · Cited by 15
“In the research of intrusion detection, Secure Water Treatment (SWaT) [20] is one of the more common industrial control datasets.”
BackgroundMethodYao, M., Tao, D., Qi, P., & Gao, R. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3486688 · Cited by 15
“[36] and Water Distribution (WADI) [36] are two datasets that contain recordings from a scaled-down water treatment plant and reservoir tanks for scientific research.”
A Multi-scale Patch Mixer Network for Time Series Anomaly Detection
Wang, Q., Zhu, Y., Sun, Z., Li, D., & Ma, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109687 · Cited by 15
Automated Anomaly Detection in CPS Log Files
Schmidt, T., Hauer, F., & Pretschner, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-54549-9_12 · Cited by 15
Kim, H., Kim, S., Jo, W., Kim, K.-H., & Shon, T. (2021) · IEEE Access · doi:10.1109/access.2021.3080081 · Cited by 14
“…to CPIS security have been actively conducted, such as the definition of normal and abnormal operations of CPISs [7], testbed construction considering the real operating environment, normal/anomaly data generation/collection [8]–[10], and encryption and authentication in field networks [11]–[13].”
Background(2021) · doi:10.1007/978-3-030-78086-9_6 · Cited by 19
Survey of deep learning approaches for securing industrial control systems: A comparative analysis
Aslam, M. M., Tufail, A., & Irshad, M. N. (2025) · Cyber Security and Applications · doi:10.1016/j.csa.2025.100096 · Cited by 14
An intelligent intrusion detection system for cyber-physical systems using GAN-LSTM networks
Siddique, M. S., Khan, M. A. R., Ahammad, I., Nath, N., Das, J. R., & Rahman, F. (2025) · Franklin Open · doi:10.1016/j.fraope.2025.100281 · Cited by 18
TFDPM: Attack detection for cyber–physical systems with diffusion probabilistic models
Yan, T., Zhou, T., Zhan, Y., & Xia, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109743 · Cited by 15
Heterogeneous Sensor Fault Detection for Networked Systems Based on a Graph Transformer
Wu, W., Pang, A., & Yang, W. G. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3345377 · Cited by 14
Xue, B., Gao, X., Li, B., Zhai, F., Lu, J., Yu, J., Fu, S., & Xiao, C. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106395 · Cited by 15
Altın, M., & CAKIR, A. M. (2024) · IEEE Access · doi:10.1109/access.2024.3415088 · Cited by 14
Liu, J., Sha, Y. D., Zhang, W., Yan, Y. G., & Liu, X. (2024) · Sensors · doi:10.3390/s24186131 · Cited by 14
“The SWaT (Secure Water Treatment) dataset [21] is a public industrial control system security research dataset designed to simulate a real water treatment system.”
BackgroundYao, M., Tao, D., Gao, R., & Qi, P. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3421600 · Cited by 14
Using Kolmogorov–Arnold network for cyber-physical system security: A fast and efficient approach
Ghorbani, M., Ghassemi, A., Alikhani, M., Khaloozadeh, H., & Nikoofard, A. (2025) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2025.100768 · Cited by 14
Sung, D. C. L., Raman, M. R. G., & Mathur, A. P. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102532 · Cited by 14
Self-citationAnomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space
Oliveira, N. R. B. D., Sousa, N., Oliveira, J., & Praça, I. (2021) · International Conference on Security of Information and Networks (SIN) · doi:10.1109/sin54109.2021.9699339 · Cited by 14
“TABLE II NUMBER OF ATTACKS PER CATEGORY [3]”
BackgroundMethodInfluentialEvaluating Machine Learning Approaches for Cyber and Physical Anomalies in SCADA Systems
Faramondi, L., Flammini, F., Guarino, S., & Setola, R. (2023) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10224915 · Cited by 14
“compare the performance of supervised and unsupervised machine learning algorithms applied to the SWaT dataset provided by iTrust [10].”
MethodInfluentialResearch on Anomaly Detection in Wastewater Treatment Systems Based on a VAE-LSTM Fusion Model
Liu, X., Gong, Z., & Zhang, X. (2025) · Water · doi:10.3390/w17192842 · Cited by 17
“These datasets were collected and open-sourced by the iTrust research group at the Singapore University of Technology and Design (SUTD), containing both normal and attack-induced multi-dimensional sensor signal data [24,25].”
Jin, C., Yang, Z., Xiang, T., Adepu, S., & Zhou, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3234873 · Cited by 13
“As a validation of this assumption, we evaluated the min-entropy of sensor measurements in real industrial control systems based on one dataset of the operations of a real-world water treatment system [40].”
MethodSelf-citationAI for Cyberbiosecurity in Water Systems—A Survey
Sobien, D., Yardimci, M. O., Nguyen, M. B. T., Mao, W.-Y., Fordham, V., Rahman, A., Duncan, S. E., & Batarseh, F. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-26034-6_13 · Cited by 16
Machine Learning for Anomaly Detection in Industrial Environments
Grunova, D., Bakratsi, V., Vrochidou, Ε., & Papakostas, G. A. (2024) · International Conference on Electronics, Engineering Physics and Earth Science (EEPES) · doi:10.3390/engproc2024070025 · Cited by 13
SCO-RNN: A Behavioral-Based Intrusion Detection Approach for Cyber Physical Attacks in SCADA Systems
Neha, N., Priyanga, S., Seshan, S., Senthilnathan, R., & Sriram, V. S. S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-0146-3_88 · Cited by 13
Management of a Dynamic Infrastructure of Complex Systems Under Conditions of Directed Cyber Attacks
Петрович, З. Д., Lavrova, D. S., & Pavlenko, E. Y. (2020) · Journal of Computer and Systems Sciences International · doi:10.1134/s1064230720020124 · Cited by 13
“…ADJUSTABILITY To conduct experimental studies to assess the stability of CDIS, the developed modeling system used the data on the architecture, components, and principles of the automatic water distribution system in a “smart city,” as well as the data on simulated cyber attacks on the system [25].”
MethodPLCrypto: A Symmetric Cryptographic Library for Programmable Logic Controllers
Yang, Z., Bao, Z., Jin, C., Liu, Z., & Zhou, J. (2021) · IACR Transactions on Symmetric Cryptology · doi:10.46586/tosc.v2021.i3.170-217 · Cited by 14
“For example, in a water treatment system, PLC data is collected once per second [GAJM16].”
BackgroundDeepnoise: Learning sensor and process noise to detect data integrity attacks in CPS
Luo, Y., Cheng, L., Liang, Y., Fu, J., & Peng, G. (2021) · China Communications · doi:10.23919/jcc.2021.09.015 · Cited by 13
Yun, J.-H., Hwang, Y., Lee, W., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00470-5_27 · Cited by 13
Kravchik, M., & Shabtai, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01216 · Cited by 13
“Kravchik and Shabtai [23] suggested using unsupervised neural networks based on 1D CNNs and demonstrated detection 31 of 36 cyber attacks in the popular SWaT dataset [12], improving upon previously published results.”
BackgroundResultKhacha, A., Aliouat, Z., Harbi, Y., Gherbi, C., Saadouni, R., & Harous, S. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109725 · Cited by 17
Using machine learning to detect network intrusions in industrial control systems: a survey
Termanini, A., Al‐Abri, D., Bourdoucen, H., & Maashri, A. A. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00916-x · Cited by 21
“The SWaT datasets are utilized broadly in literature for security studies of ICS networks [78–82].”
Han, C., & Gim, G.-Y. (2025) · Processes · doi:10.3390/pr13092885 · Cited by 13
A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detection in Water Distribution Plant
Şahin, A. K., Çavdar, B., Doğan, R. Ö., Ayas, S., Özgenç, B., & Ayas, M. Ş. (2023) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu58738.2023.10296546 · Cited by 12
“As an extension of Secure Water Treatment (SWaT) testbed [9], WADI testbed [1] integrates some of SWaT’s reverse osmosis permeate and raw water, creating a complete and realistic water treatment, storage, and distribution network.”
MethodFast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems
Huang, M., Li, T., Li, B., Zhang, N., & Huang, H. (2023) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2023-0017 · Cited by 12
Liu, Y., Deng, W., Liu, Z., & Zeng, F. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06018-8 · Cited by 12
“The SWaT dataset includes 7 days of records under normal conditions and 4 days of records under attack conditions, making it a valuable resource for studying anomalies and security threats in industrial control systems [34].”
BackgroundHoneyJudge: A PLC Honeypot Identification Framework Based on Device Memory Testing
Zhu, H., Liu, M., Chen, B., Che, X., Cheng, P., & Deng, R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3407520 · Cited by 13
“…is derived from two sources: 1) Network messages and SSM information are obtained from a real operational Siemens PLC device in our testbed, which operates a part of SWaT control processes; 2) physical characteristics of the real-world industrial scenario are obtained from SWaT datasets [39].”
BackgroundNormal-only Anomaly detection in environmental sensors in CPS: A comprehensive review
Acquaah, Y. T., & Roy, K. (2024) · IEEE Access · doi:10.1109/access.2024.3513714 · Cited by 13
“Four publicly available datasets namely IBRL [71], Yahoo’s Anomaly Detection [127], Process Miner’s Rare Event Detection [128], and Secure Water Treatment (SWaT) [91] datasets were used to validate the proposed methodology.”
Deployment Challenges of Industrial Intrusion Detection Systems
Wolsing, K., Wagner, E., Basels, F., Wagner, P., & Wehrle, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82349-7_29 · Cited by 12
Hussain, M., Foo, E., & Suriadi, S. (2019) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit47737.2019.00037 · Cited by 12
“It is evaluated using two different datasets, QUT’s SIEMENS S7 dataset [7] and SUTD water treatment system [13].”
BackgroundMethodAhmed, C. M., Prakash, J., Qadeer, R., et al. (2020) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3395351.3399364 · Cited by 12
A Machine Learning approach to Intrusion Detection in Water Distribution Systems – A Review
Mboweni, I. V., Abu-Mahfouz, A. M. I., & Ramotsoela, D. T. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589237 · Cited by 11
“This can be demonstrated by the secure water treatment (SWaT) testbed with distinct attacks [13], [14].”
BackgroundNaito, S., Taguchi, Y., Nakata, K., & Kato, Y. (2021) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw53433.2021.00072 · Cited by 13
“The SWaT dataset 1 is a scaled-down version of an actual industrial water treatment plant producing filtered water [22].”
BackgroundDetection of Cyber Attacks on a Water Treatment Process
Al-Dhaheri, M., Zhang, P., & Mikhaylenko, D. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.204 · Cited by 12
A data generation framework for extremely rare case signals
Chalongvorachai, T., & Woraratpanya, K. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07687 · Cited by 12
“Note that the lack of ability to determine the output number as mentioned in Section 2.5, as a consequence, makes it impossible for the DAGAT model to test in the controlled environment experiment For ADGAS, we duplicated the setting from previous experiments.”
MethodInfluentialAgent based dynamic thresholding for adaptive anomaly detection using reinforcement learning
Yang, X., Howley, E., & Schukat, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10536-0 · Cited by 11
“It consists of 11-day continuous operations where 7 days are under normal scenarios and 4 days are under attack scenarios [32].”
Liu, H., Luo, W., Han, L., Gao, P., Yang, W., & Han, G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3362398 · Cited by 14
An Unsupervised Long- and Short-Term Sparse Graph Neural Network for Multisensor Anomaly Detection
Miao, Q., Wang, D., Xu, C., Zhan, J., & Wu, C. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3383665 · Cited by 11
“1) Securewater treatment (SWaT) [24] and water distribution (WaDI) [24] 1 are two datasets from a real water treatment plant, this dataset includes sensor values (water level, flow, etc.) and actuator operations (valves and pumps).”
BackgroundAnomaly detection in cyber-physical systems using actuator state transition model
Pandey, R. K., & Das, T. K. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02128-x · Cited by 11
“The iTrust Center for Cyber Security at the Singapore University of Technology and Design (SUTD) hosts the SWaT testbed [15].”
Catillo, M., Pecchia, A., & Villano, U. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101519 · Cited by 14
Yao, M., Tao, D., Qi, P., & Gao, R. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3536306 · Cited by 11
“2-3) SWaT [25] & WADI [25]: The Secure Water Treatment (SWaT) and the Water Distribution (WADI) are two datasets that record the operation status of attacked water treatment systems.”
Empowered Cyber–Physical Systems security using both network and physical data
Canonico, R., Esposito, G., Navarro, A., Romano, S. P., Sperlí, G., & Vignali, A. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104382 · Cited by 11
TiTAD: Time-Invariant Transformer for Multivariate Time Series Anomaly Detection
Liu, Y., Wang, W., & Wu, Y. (2025) · Electronics · doi:10.3390/electronics14071401 · Cited by 12
A novel online state-based anomaly detection system for process control networks
Farsi, H., Fanian, A., & Taghiyarrenani, Z. (2019) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2019.100323 · Cited by 11
Anomaly Detection using Clustered Deep One-Class Classification
Kim, Y.-H., & Kim, H. K. (2020) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis50894.2020.00034 · Cited by 11
“In this paper, the data collection step is simplified using a publicly available dataset named HIL based Augmented ICS testbed (HAI) dataset [19] and Secure Water Treatment (SWaT) dataset [20].”
MethodTama, B. A., Lee, S. Y., & Lee, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09767-y · Cited by 14
Anomaly Detection in Cyber-Physical Systems based on BiGRU-VAE
Alguliyev, R. M., Sukhostat, L. V., & Mammadov, A. (2022) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/aict55583.2022.10013581 · Cited by 13
“This paper considers the secure water treatment (SWaT) dataset to evaluate the proposed CPS anomaly detection model [5, 6].”
MethodP 2 O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants
Kulkarni, A., Yardimci, M., Sikder, M. N. K., & Batarseh, F. A. (2023) · Journal of Environmental Engineering · doi:10.1061/joeedu.eeeng-7266 · Cited by 15
Zhao, X., Liu, P., Mahmoudi, S., Garg, S., Kaddoum, G., & Hassan, M. M. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.07.013 · Cited by 14
Towards ‘Verifying’ a Water Treatment System
Wang, J., Sun, J., Jia, Y., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95582-7_5 · Cited by 11
“A dataset of SWaT has been published by the iTrust lab in Singapore University of Technology and Design [27,10].”
BackgroundMethodInfluentialLi, Q., Ji, Y., Sun, L., Zhang, N., & Yang, T. (2026) · Computers in Industry · doi:10.1016/j.compind.2026.104445 · Cited by 10
HFN: Heterogeneous feature network for multivariate time series anomaly detection
Zhan, J., Wu, C., Yang, C., Miao, Q., & Ma, X. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120626 · Cited by 10
He, S., Li, G., Wang, J., Xie, K., & Sharma, P. K. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02212-5 · Cited by 10
An anomaly-based approach for cyber–physical threat detection using network and sensor data
Canonico, R., Esposito, G., Navarro, A., Romano, S. P., Sperlí, G., & Vignali, A. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108087 · Cited by 10
STMBAD: Spatio-Temporal Multimodal Behavior Anomaly Detector for Industrial Control Systems
Luo, J., Yan, C. Y., Cai, J., Fang, W., & Zheng, W. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3528559 · Cited by 10
“1) SWaT [34]: SWaT is a water treatment testbed for re-searchinthefieldofcybersecurity.”
Frequency-enhanced and decomposed transformer for multivariate time series anomaly detection
Li, S., Wang, Z., Wang, X., Yin, Z., & Yao, M. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06441-1 · Cited by 10
Zeng, G., Shao, J., Lu, K., Geng, G., & Weng, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3586479 · Cited by 15
“(1) SWaT [29]: The SWaT (Secure Water Treatment) dataset originates from physical testbed systems for water treatment managed by Singapore’s Public Utility Board.”
Unsupervised and incremental learning orchestration for cyber‐physical security
Reis, L. H. A., Murillo, A., Rueda, S. J., et al. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4011 · Cited by 10
“8 Previous proposals focused on the CPS modeling 20 or on the pattern extraction from the dataset.”
MethodInfluentialAlqurashi, S., Shirazi, H. M., & Ray, I. (2021) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms53705.2021.9704986 · Cited by 10
“Similarly, the Water Distribution (WADI) dataset [16] contains 16 days of logs of 123 industrial sensors and actuators.”
MethodA Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed
Koutroulis, G., Mutlu, B., & Kern, R. (2022) · Sensors · doi:10.3390/s23010257 · Cited by 12
“reader to the initial SWaT paper [54], which lists all details regarding the previously mentioned attack scenarios.”
BackgroundMethodInfluentialDiscovering a data interpreted petri net model of industrial control systems for anomaly detection
Hussain, M., Fidge, C., Foo, E., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120511 · Cited by 10
Madhawa, S., Balakrishnan, P., & Umamakeswari, A. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169425 · Cited by 10
“The proposed IDS is verified using a real-time dataset containing both normal and attack data related to payload changes in a water treat-ment facility which is obtained from iTrust, a research center [8].”
A Comparative Analysis of Emulated and Real IEC-104 Spontaneous Traffic in Power System Networks
Lin, C.-Y., & Nadjm‐Tehrani, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69781-5_14 · Cited by 10
Abid, A., Jemili, F., & Korbaa, O. (2023) · Journal of Information and Telecommunication · doi:10.1080/24751839.2023.2239617 · Cited by 9
“The SWaT dataset (Goh et al., 2017) is a scaled-down version of a real industrial water treatment plant that utilizes ultra fi ltration and reverse osmosis membrane units to produce 5 gallons per minute of fi ltered water.”
BackgroundMethod[SoK] Evaluations in Industrial Intrusion Detection Research
Lamberts, O., Wolsing, K., Wagner, E., Pennekamp, J., Bauer, J., Wehrle, K., & Henze, M. (2023) · Journal of Systems Research · doi:10.5070/sr33162445 · Cited by 10
“As a consequence of SWaT’s large testbed and slow process cycle, it was reported that it could take some time after a cyberattack was initiated until effects become visible, and these effects can exceed the actual attack until the process stabilizes again, making determining the range of attacks…”
BackgroundInfluentialModel based anomaly detection in cyber physical power systems
Awad, A. M., Alheeti, K. M. A., & Najem, A. K. A. H. (2024) · AIP conference proceedings · doi:10.1063/5.0234148 · Cited by 9
HGCNN-LSTM: A Data-driven Approach for Cyberattack Detection in Cyber-Physical Systems
Abinash, S., Srivatsan, N., Hemachandran, S. K., & Priyanga, S. (2025) · SN Computer Science · doi:10.1007/s42979-024-03613-7 · Cited by 9
“These sensors fall into four main categories: flow meters (FIT), level transmitters (LIT), pressure meters (PIT), and chemical properties analyzers (AIT) [27].”
Kuchař, K., & Fujdiak, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104395 · Cited by 10
Chen, J., Jin, J., Zhang, L., Wang, C., & Huang, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103213 · Cited by 10
Anomaly Detection on Time-series Logs for Industrial Network
Chen, L., Kuang, X., Xu, A., Yang, Y., & Suo, S. (2020) · International Conference on Smart BlockChain (SmartBlock) · doi:10.1109/smartblock52591.2020.00022 · Cited by 9
“SWaT [10][21] is a simulated industrial control environment Designed by the Singapore University of Technology and Design which is used to support the research and development of cyber-physical systems (CPS).”
MethodIntrusion Detection for Industrial Control Systems by Machine Learning using Privileged Information
Pordelkhaki, M., Fouad, S., & Josephs, M. B. (2021) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi53945.2021.9624757 · Cited by 12
“In this dataset, attackers hijacked data packets through the Level 1 communication link of Figure1, manipulated the sensor data in the packets and sent them to the PLCs [24].”
BackgroundMethodInfluentialAshok, A., & Edgar, T. W. (2021) · IEEE International Symposium on Technologies for Homeland Security (HST) · doi:10.1109/hst53381.2021.9619851 · Cited by 9
Fuzzy Controller-empowered Autoencoder Framework for anomaly detection in Cyber Physical Systems
Gupta, K. D., Singhal, K., Sharma, D. K., Sharma, N., & Malebary, S. J. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108685 · Cited by 9
Are we certain it’s anomalous?
Flaborea, A., Prenkaj, B., Munjal, B., Sterpa, M. A., d’Aragona, D. A., Podo, L., & Galasso, F. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00291 · Cited by 10
“We consider for analysis the SWaT [23, 53] and CASAS [12, 13] datasets.”
MethodA Robust Framework for Evaluation of Unsupervised Time-Series Anomaly Detection
Güngör, O., Rios, A., Mudgal, P., Ahuja, N., & Rosing, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78395-1_4 · Cited by 10
A modified densenet approach with nearmiss for anomaly detection in industrial control systems
Ayas, S., & Ayas, M. Ş. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11618-0 · Cited by 9
“Dataset obtained from the SWaT testbed is presented and provided in [6] for the researchers to make comparison with other studies using the same ICS.”
MethodBenchmarking Machine Learning based Detection of Cyber Attacks for Critical Infrastructure
Kumar, A., & Choi, B. J. (2022) · International Conference on Information Networking · doi:10.1109/icoin53446.2022.9687293 · Cited by 9
System for Operational Technology Attack Detection in Industrial IoT
Charilaou, C., Ioannou, C., & Vassiliou, V. (2022) · Mediterranean Communication and Computer Networking Conference (MedComNet) · doi:10.1109/medcomnet55087.2022.9810446 · Cited by 9
“More details can be found in [7] .”
BackgroundMethodInfluentialErba, A., & Tippenhauer, N. O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03586 · Cited by 9
“sensor readings and actuator states recorded from ICS testbed or process simulation [35], [22], [27], [16].”
Method
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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