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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
Bay, S. D., Kibler, D. F., Pazzani, M. J., & Smyth, P. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/380995.381030 · Cited by 234
An Investigation into the Response of a Water Treatment System to Cyber Attacks
Adepu, S., & Mathur, A. P. (2016) · Proceedings - IEEE International Symposium on High-Assurance Systems Engineering/Proceedings · doi:10.1109/hase.2016.14 · Cited by 88
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Items 301–400 of 734
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems
Macas, M., Wu, C., & Fuertes, W. (2023) · International Conference on Web Information Systems and Technologies · doi:10.5220/0012264000003584 · Cited by 10
“In contrast, (Macas and Wu, 2019) developed an attention-based Convolutional LSTM Encoder-Decoder (ConvLSTM-ED) model, which analyzed the entire SWaT testbed (Goh et al., 2017a).”
BackgroundMethodInfluentialSAKMR: Industrial control anomaly detection based on semi-supervised hybrid deep learning
Tang, S., Ding, Y., Zhao, M., et al. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-023-01586-7 · Cited by 8
“We use two commonly used public datasets KDDCUP99 [22] and SWAT [23] to evaluate the effectiveness of SAKMR in industrial control anomaly detection.”
Towards the Testbed and Dataset for Analysis of Water Treatment Systems Security
Владимировна, Ф. Е., Novikova, E. S., Danilov, A., & Saenko, I. B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7814-4_37 · Cited by 8
Meli, D. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711445 · Cited by 10
“For full details about them, we refer the reader respectively to [16] and https://sites.google.com/diag.uniroma1.it/robsec-data.”
BackgroundMethodAnomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges
Kuchař, K., & Fujdiak, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3512857 · Cited by 11
Autoregressive Density Estimation Transformers for Multivariate Time Series Anomaly Detection
Belay, M. A., Rasheed, A., & Rossi, P. S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888728 · Cited by 8
“1) SWaT [19], [20] 2) WADI [21] We employ two key pre-processing steps (downsampling and feature normalization) on the input data prior to utilizing it in the framework.”
NGMO: A Novel Geometric Mean Optimizer for Intrusion Detection in Industrial Cyber-Physical Systems
Yao, Y., Zhang, Z., Zhao, K., Wang, P., & Wu, R. (2025) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2025.3556034 · Cited by 10
“Secure Water Treatment (SWaT) [29]: The dataset was collected from a six-stage safe water treatment testbed, which simulates a scaled-down version of a real-world industrial water treatment plant.”
Image-Based Intrusion Detection in Network Traffic
Golubev, S., & Novikova, E. S. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-29104-3_6 · Cited by 8
CB-GAN: Generate Sensitive Data with a Convolutional Bidirectional Generative Adversarial Networks
Hu, R., Li, D., Ng, S.-K., & Zheng, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30678-5_13 · Cited by 8
Explainable Articial Intelligence for Cybersecurity in Smart Manufacturing
Bac, T. P., Ha, D. T., Tran, K. D., & Tran, K. P. (2023) · Springer series in reliability engineering · doi:10.1007/978-3-031-30510-8_10 · Cited by 8
Self-Supervised Modular Architecture for Multi-Sensor Anomaly Detection and Localization
Belay, M. A., Rasheed, A., & Rossi, P. S. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00226 · Cited by 8
“To evaluate the performance of our proposed architecture, we employ six baseline state-of-the-art anomaly detection methods.”
MethodHow to Fingerprint Attack Traffic against Industrial Control System Network
Sheng, C., Yao, Y. K., Yang, W., et al. (2019) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iciai.2019.8850807 · Cited by 8
“The SWaT dataset is developed by iTrust on a six-stage Secure Water Treatment (SWaT) testbed [21].”
MethodGao, L., Deng, X., & Yang, W.-M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05733-0 · Cited by 8
“The secure water treatment (SWaT) [14, 15] data set was selected to evaluate the proposed model.”
BackgroundMethodAnomaly Detection for CPS via Memory-Augmented Reconstruction and Time Series Prediction
Sun, Z., & Li, J. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00080 · Cited by 8
“SWAT: This data set is a CPS test stand at the Singapore University of Technology and Design (SUTD), which is a replica of a large water treatment plant [17].”
MethodChallenges and Pitfalls in Generating Representative ICS Datasets in Cyber Security Research
Mitseva, A., Thierse, P., Hoffmann, H., Er, D., & Panchenko, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25460-4_22 · Cited by 8
Machine learning and user interface for cyber risk management of water infrastructure
Neshenko, N., Bou‐Harb, E., Furht, B., et al. (2023) · Risk Analysis · doi:10.1111/risa.14209 · Cited by 8
“We evaluated proposed approach using data collected by a small-scale water treatment plant that encompasses six stages of the water treatment process (P1 through P6) controlled by a dedicated Programmable Logic Controller (PLC) (Figure 6) (Goh et al., 2016).”
MethodDetecting Cyberattacks in Industrial Control Systems Using Convolutional Neural NetworksPreprint
Kravchik, M., & Shabtai, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08110 · Cited by 14
“Recently, unsupervised machine learning was shown to be effective [7] [19] for detecting cyber attacks using data obtained from a dedicated water plant testbed (SWaT) [6] which was built to support research related to the design of secure cyber physical systems (CPSs).”
BackgroundMethodInfluentialMSGAN: multi-stage generative adversarial network-based data recovery in cyber-attacks
Tian, B., Lai, Y., Sun, M., Wang, Y., & Liu, J. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08791-8 · Cited by 7
Almazyad, I., Shao, S., Hariri, S., et al. (2023) · ACS/IEEE International Conference on Computer Systems and Applications · doi:10.1109/aiccsa59173.2023.10479312 · Cited by 7
“Most existing cybersecurity research in the water domain uses the Secure Water Treatment (SWaT) dataset obtained from a testbed developed by the Singapore University of Technology and Design [13].”
MethodResilience Assessment of Multi-Layered Cyber-Physical Systems
Dagnas, R., Barbeau, M., Joaquin Garcia-alfaro, B., et al. (2024) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking62109.2024.10619809 · Cited by 7
“SWaT is divided into six stages: Pumping , Chemical Dosing , Ultrafiltration (UF) , Dechlorination , Reverse Osmosis (RO) , Final stage and Backwash of the UF membrane [27].”
BackgroundDeep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems
Kim, M.-S. (2024) · Mathematics · doi:10.3390/math12243900 · Cited by 7
Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation
Park, H., & Jang, H. (2024) · Sensors · doi:10.3390/s24248169 · Cited by 8
Jia, X., Cao, D., Zhuang, N., Peng, W., Zhao, B., Peng, X., Li, H., & Shen, C. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107903 · Cited by 7
Real-time detection of deception attacks in cyber-physical systems
Cai, F., & Koutsoukos, X. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00677-z · Cited by 7
“In this section, we evaluate the proposed approach for detecting (1) sensor replay attacks using an advanced emergency braking system (AEBS) implemented in the CARLA simulator [10], (2) controller deception attacks using an autonomous car racing example implemented in OpenAI Gym [4], and (3) deception attacks using a…”
BackgroundMethodInfluentialAnomaly Detection in Industrial Multivariate Time-Series Data With Neutrosophic Theory
Liu, P., Han, Q., Wu, T., & Tao, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3262612 · Cited by 7
“For these two data sets, the samples of the previous system startup part in the training data are deleted to ensure that the data in the training process come from the stable state of the system [28].”
MethodShapelet-Based Sensor Fault Detection and Human-Centered Explanations in Industrial Control System
Lim, S., Kim, J., & Lee, T. (2023) · IEEE Access · doi:10.1109/access.2023.3339500 · Cited by 7
“This dataset targets the protection of Cyber-Physical Systems (CPS) such as those for water treatment, power generation and distribution, and oil and natural gas refinement [33].”
Anomaly Detectors for Multivariate Time Series: The Proof of the Pudding is in the Eating
Wenig, P., Schmidl, S., & Papenbrock, T. (2024) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew61823.2024.00018 · Cited by 7
Yang, J., Sheng, Y.-Q., Wang, J., & Ni, H. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-022-2149-y · Cited by 8
Enhanced Cyber-Physical Security through Deep Learning Techniques.
Carrasco, M. A. M., & Wu, C. (2019) · Cited by 7
Mathematical Methods for Implementing Homeostatic Control in Digital Production Systems
Pavlenko, E. Y., & Poltavtseva, M. A. (2020) · Lecture notes in networks and systems · doi:10.1007/978-3-030-64430-7_1 · Cited by 7
A Fully-Blind False Data Injection on PROFINET I/O Systems
Alsabbagh, W., & Langendörfer, P. (2021) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576496 · Cited by 7
Indri, P., Bartoli, A., Medvet, E., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02056-8_3 · Cited by 7
Anomaly Detection for Process Industry using Class-Dependent Temperatures Loss
Li, Z., Yang, Z., Tang, D., Peng, S., & Li, J. (2023) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs57501.2023.10150495 · Cited by 7
“We evaluate our method on two real-world datasets: Secure Water Treatment (SWaT) and Water Distribution (WADI) [23].”
BackgroundMethodInfluentialFinding Causally Different Tests for an Industrial Control System
POSKITT, C. M., Chen, Y., Sun, J., & Jiang, Y. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00215 · Cited by 7
“Experimental evaluations of these fuzzers show they can generate test suites that cover a similar range of testing goals to those of an established benchmark [19] for a water treatment testbed.”
BackgroundMethodInfluentialA Stitch in Time Saves Nine: Enabling Early Anomaly Detection with Correlation Analysis
Ang, Y., Huang, Q., Tung, A. K. H., & Huang, Z. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00143 · Cited by 8
“For example, the sampling rate of sensors from a water treatment plant is around 1 Hz [29], [57], while the ones from a bearing system can be up to 25.6k Hz [82].”
BackgroundA derivative, integral, and proportional features extractor for fault detection in dynamic processes
Leoni, J., Gelmini, S., Panzani, G., et al. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107510 · Cited by 7
Distributed runtime verification of metric temporal properties
Ganguly, R., Xue, Y., Jonckheere, A., et al. (2023) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2023.104801 · Cited by 7
Machine Learning on Dynamic Graphs: A Survey on Applications
Fard, S. H. (2023) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm59094.2023.00012 · Cited by 8
“MST-GAT has been evaluated on benchmark datasets including MSL [98], SMAP [98], SWaT [99], and WADI [100] and has outperformed the state-of-the-art baselines.”
MethodAlimi, O. A., Ouahada, K., Abu-Mahfouz, A. M. I., Rimer, S., & Alimi, K. O. A. (2021) · IEEE AFRICON · doi:10.1109/africon51333.2021.9570951 · Cited by 7
Xiao, Q., Shao, S., & Wang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08306 · Cited by 12
“We use four public multivariate time-series datasets to evaluate our model: Soil Moisture Active Passive (SMAP) satellite, Mars Science Laboratory (MSL) rover Datasets (Hundman et al. 2018), Server Machine Dataset (SMD) (Su et al. 2019) and Secure Water Treatment (SWaT) Dataset (Goh et al. 2016).”
BackgroundMethodInfluentialSGFM: Conditional Flow Matching for Time Series Anomaly Detection With State Space Models
He, Y., Yan, T., Zhan, Y., Feng, Z., & Xia, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3439672 · Cited by 6
Detecting Multivariate Time Series Anomalies With Cascade Decomposition Consistency
Li, R., Liu, Z., Zhu, X., Li, L., & Cao, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547479 · Cited by 6
“Experiments were conducted on five datasets: PSM [8], SWaT [45], WADI [46], MSL [31], and SMD [32].”
Maghanaki, M., Shahin, M., Keramati, S., et al. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6030090 · Cited by 6
“IIoT systems amplify these risks: incidents such as the 2021 Colonial Pipeline disruption [67] and attacks on water treatment facilities [68] highlight how resource-constrained and weakly secured devices can be weaponized to disrupt essential services, contaminate supply systems, and trigger…”
conDENSE: Conditional Density Estimation for Time Series Anomaly Detection
Moore, A. K., & Morelli, D. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14849 · Cited by 7
“• Secure Water Treatment (SWaT): provided by iTrust, this dataset is taken from a scaled-down water treatment testbed (Goh, et al., 2016).”
MethodDutta, A. K., Mukhoty, B., & Shukla, S. K. (2021) · ACM Workshop on CPS&IoT Security and Privacy (CPSIoTSec) · doi:10.1145/3462633.3483978 · Cited by 6
“1 Secure Water Treatement(SWaT) Dataset [9].”
Time Series Anomaly Detection for Smart Grids via Multiple Self-Supervised Tasks Learning
Zhang, Z., Zhao, L., Cai, D., Feng, S., Miao, J., Guan, Y., Tao, H., & Cao, J. (2022) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg55886.2022.00057 · Cited by 6
“Detecting security problems in a water treatment plant [20], in which hundreds of variables should be taken into consideration.”
BackgroundMascolini, A., Gaiardelli, S., Ponzio, F., Dall’Ora, N., Macii, E., Vinco, S., Cataldo, S. D., & Fummi, F. (2023) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon51785.2023.10311726 · Cited by 6
“Among the others: Secure Water Treatment (SWaT) [5], Water Distribution Testbed (WADI) [6], Server Machine Dataset (SMD) [7], Mars Science Laboratory (MSL) [8], Soil Moisture Active Passive (SMAP) [9].”
BackgroundGCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection
Xing, S., Niu, J., & Ren, T. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00187 · Cited by 6
“For instance, in the SWaT dataset [8], a state change in UV401 affects AIT502 , which in turn influences downstream series like AIT503 and AIT504 with some latency.”
BackgroundMethodFusing Design and Machine Learning for Anomaly Detection in Water Treatment Plants
Raman, M. R. G., & Mathur, A. P. (2024) · Electronics · doi:10.3390/electronics13122267 · Cited by 7
Self-citationChallenges in Secure Engineering of Critical Infrastructure Systems
Adepu, S., Kang, E., & Mathur, A. P. (2019) · IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) · doi:10.1109/asew.2019.00030 · Cited by 6
“for different CPS testbeds [5] and used in the evaluation”
MethodSelf-citationParameterless Semi-supervised Anomaly Detection in Univariate Time Series
Iegorov, O., & Fischmeister, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67658-2_37 · Cited by 6
The detection of sensor signal attacks in industrial control systems
Nedeljkovic, D. M., Jakovljević, Ž., & Miljković, Z. (2020) · FME Transaction · doi:10.5937/fmet2001007n · Cited by 6
“It was built to provide data from the real system useful for research mainly in the field of cybersecurity [15].”
BackgroundHMAKE: Legacy-Compliant Multi-factor Authenticated Key Exchange from Historical Data.Preprint
Jin, C., Yang, Z., Adepu, S., et al. (2019) · IACR Cryptology ePrint Archive · Cited by 6
Self-citationLi, J. W., Yu, Z., Jiang, Q., & Cao, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3548251 · Cited by 5
“1) Datasets: Four datasets were employed to evaluate the effectiveness of the proposed method: Secure Water Treatment (SWaT) [56], the Mars Science Laboratory Rover (MSL) [3], Server Machine (SMD) [1], and the Penicillin Simulation dataset [57].”
Assessing the Graph Structure Learning in Graph Deviation Networks
Ozen, C., Nowaczyk, S., Tiwari, P., & Pashami, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91398-3_8 · Cited by 5
Interpretable transformation and analysis of timelines through learning via surprisability
Mokryn, O., Lazebnik, T., & Shoshan, H. B. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0269365 · Cited by 5
Yang, L., Yao, Y., Yang, D., Yang, W., & Hao, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115380 · Cited by 5
Alarm-Budgeted Event-Level Evaluation Of Ics Anomaly Detection: Lessons From Swat And Wadi
Heydari, V., & Nyarko, K. (2026) · IEEE Access · doi:10.1109/access.2026.3659034 · Cited by 4
“The commonly used SWaT stream is sampled at 1 Hz and includes multiple days of normal operation followed by an attack period with labeled incidents [7].”
Jia, M., Jiang, L., Hu, J., Liu, Y., & Chen, T. (2024) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3888 · Cited by 5
“In this section, the performance and effectiveness of SFC-DAE is demonstrated using two processes: the Damadics Data Set (DMDS) of Lublin Sugar Factory 34 and the Secure Water Treatment System (SWaT), 35 which include different anomaly scenarios for testing.”
Alkulaib, L. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10826127 · Cited by 5
Advanced Predictive Techniques for detection of Cyber-Attacks in Water Infrastructures
Toe, T. T., Yi, L. H., & Josephlal, E. F. M. (2020) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc50002.2020.9259535 · Cited by 5
“To create our models, we used the iTrust Labs Secure Water Treatment (SWaT) Dataset [9].”
MethodVNWTS: A Virtual Water Chlorination Process for Cybersecurity Analysis of Industrial Control Systems
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., & Porcel-Bustamante, J. (2021) · International Conference on Security of Information and Networks (SIN) · doi:10.1109/sin54109.2021.9699375 · Cited by 5
“We could not find such attacks and their associated features in the existing work such as the data logs related to the Secure Water Treatment System (SWaT) testbed [25] e.g. [16], [17], [18] - [20] and [21].”
BackgroundSupervised learning based intrusion detection for SCADA systems
Alimi, O. A., Ouahada, K., Abu-Mahfouz, A. M. I., Rimer, S., & Alimi, K. O. A. (2022) · IEEE Nigeria International Conference on Disruptive Technologies for Sustainable Development (NIGERCON) · doi:10.1109/nigercon54645.2022.9803101 · Cited by 5
“For more details about the procedures involved in the generation of the dataset, readers are referred to [17]-[19].”
BackgroundExploring the Quantitative Resilience Analysis of Cyber-Physical Systems
Dagnas, R., Barbeau, M., Boutin, M., et al. (2023) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking57963.2023.10186355 · Cited by 5
“As a use-case, we choose the SWaT CPS [13], [27].”
Security through Digital Twin-Based Intrusion Detection: A SWaT Dataset Analysis
Bozdal, M. (2023) · doi:10.1109/isctrkiye61151.2023.10336137 · Cited by 5
“Addressing these concerns, this research investigates the application of digital twin to enhance security, specifically as an Intrusion Detection System (IDS), with a focus on the Secure Water Treatment (SWaT) [7] system.”
BackgroundHybrid Cyber-Attack Detection Model on Cyber-Physical Systems Using Machine Learning Techniques
Gulzar, Q., & Mustafa, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6547-2_16 · Cited by 5
Securing Industrial Control Systems (ICS) Through Attack Modelling and Rule-Based Learning
Mehmood, M., Baig, Z. A., & Syed, N. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10426882 · Cited by 5
“Goh et al. [6] explained its dataset that is collected from a small-scale cyber-physical system.”
BackgroundNovikova, E. S., Владимировна, Ф. Е., Danilov, A., & Saenko, I. B. (2024) · SN Computer Science · doi:10.1007/s42979-024-02704-9 · Cited by 5
“…laboratory rover) [20] Server machine dataset GRU network and variational AE with linear gaussian state space model (SSM) Neshenko et al. [21] SWaT [15] GAN Lin et al. [22] SWaT [15] TABOR (Probabilistic deterministic real timed automaton) Goetz et al. [23] Own dataset from rotary table dispenser…”
InfluentialEA-GAT: Event aware graph attention network on cyber-physical systems
Yağcı, M. Y., & Aydın, M. A. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104097 · Cited by 5
Tafazoli, S., Lu, Y., Wu, R., Srinivas, T. V. A., Cruz, H. D., Mercer, R., & Keogh, E. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02107-5 · Cited by 5
“The problem is that the metric for computing a success can vary greatly from paper to paper, making the published results incommensurate [13, 14, 29, 41, 53].”
Accurate Anomaly Detection Leveraging Knowledge-enhanced GAT
Li, Y., Zhou, Z., Deng, S., Sun, X., Xue, X., Yangui, S., & Gaaloul, W. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00077 · Cited by 5
“1, the SWaT water treatment process involves six sub-workflows (SW1 to SW6) that include raw water supply, pretreatment, filtration, disinfection, impurity reduction, and storage for distribution [18] .”
MethodInterdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection
Wang, Y., Sun, H., Wang, C., Zhu, M., Wang, J., Tang, W., Qi, Q., Zhuang, Z., & Liao, J. (2024) · Proceedings · doi:10.1109/icdm59182.2024.00107 · Cited by 7
“E XPERIMENT A. Experiment Setting 1) Dataset: We evaluate MADGA on five real-world MTS datasets with labeled anomalies: SWaT [16], WADI [1], PSM [17], MSL [18], and SMD [10].”
MethodGraph Optimization via Decoupled Edge Tuning for Efficient Industrial Anomaly Detection
Wang, K., Lu, S., Liu, Y., & Wang, B. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3567671 · Cited by 5
“The following datasets are used to evaluate the LGSA’s anomalydetectionability:SecureWaterTreatment(SWaT)[46], WAterDIstribution(WADI),CriticalInfrastructureSecurity Showdown (CISS), Battle of the Attack Detection Algorithms [47] and Petrol-Chemical Process (PCP).”
Intrusion Detection in Binary Process Data: Introducing the Hamming-distance to Matrix Profiles
Antón, S. D. D., & Schotten, H. D. (2020) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom49955.2020.00065 · Cited by 5
“51 sensors and actuators provided information about the process that could be used to detect the attacks, a complete list is provided by Goh et al. [28].”
BackgroundMethodAdversarial attacks and mitigation for anomaly detectors of cyber-physical systems
Jia, Y., Wang, J., POSKITT, C. M., Chattopadhyay, S., Sun, J., & Chen, Y. (2021) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 5
3DVAE-ERSG: 3D Variational Autoencoder for Extremely Rare Signal Generation
Chalongvorachai, T., & Woraratpanya, K. (2021) · doi:10.1109/icitee53064.2021.9611955 · Cited by 5
One IDS Is Not Enough! Exploring Ensemble Learning for Industrial Intrusion Detection
Wolsing, K., Kus, D., Wagner, E., Pennekamp, J., Wehrle, K., & Henze, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51476-0_6 · Cited by 5
“In total, SWaT, which models a water treatment plant, contains 36 cyberattacks, and WADI, which represents a water distribution testbed, contains 14 attacks.”
MethodInfluentialMachine Learning in Industrial Control System Security: A Survey
Jiang, D., & Zhao, J. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-32-9698-5_35 · Cited by 5
Dynamic Attack Scoring Using Distributed Local Detectors
Zohrevand, Z., & Glässer, U. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054264 · Cited by 5
“Water is purified through a six-stage process, comprising control and physical components [31].”
BackgroundNuclear power plant cybersecurity
Zhang, F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818190-4.00021-8 · Cited by 5
Anomaly Detection via Semantically Conjugate View Learning on Industrial Temporal Data
Wang, K., Lu, S., Wang, B., & Zhang, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3527694 · Cited by 7
“The evaluation is performed over three state-of-the-art datasets subtly designed and developed to facilitate research in ICS security: 1) the secure water treatment (SWaT) [38]; 2) water distribution (WADI); and 3) critical infrastructure security showdown (CISS).”
Malits, R., & Mendelson, A. (2025) · Water · doi:10.3390/w17030321 · Cited by 4
A time–frequency contrastive learning model for anomaly detection in multivariate time series
Zhāng, W., Li, X., Li, J., Ma, J., Pengfei, K., Zhang, S., & Liu, Y. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02119-w · Cited by 5
EdgeConvFormer: An Unsupervised Anomaly Detection Method for Multivariate Time Series
Liu, J., Li, Q., An, S., Ezard, B., & Li, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78128-5_24 · Cited by 4
Gmad: multivariate time series anomaly detection based on graph matching learning
Kong, J., Wang, K., Jiang, M., & Tao, X. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02482-z · Cited by 4
“This includes the Secure Water Treatment (SWaT) [51] dataset, 1 Pooled Server Metrics (PSM) [52] dataset, 2 Server Machine Dataset (SMD) [12] dataset, 3 and Water Distribution (WADI) [53] dataset.”
Anomaly Detection Using Graph-Based Autoencoder with Graph Structure Learning Layer
Ahmad, A., Kovalenko, A., & Makarov, I. (2024) · IEEE International Symposium on Logistics and Industrial Informatics (LINDI) · doi:10.1109/lindi63813.2024.10820392 · Cited by 6
Chen, D., Xie, Z., Yu, W. W. H., & Hu, Q. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3602182 · Cited by 4
“Two versions of the PS dataset, namely, the long-tailed and balanced datasets, are 2) SWaT Data: The SWaT dataset was acquired from a down-scaled water treatment plant crafted to replicate the operational characteristics of large, modern urban plants [53].”
Hierarchical GNN Message Passing for Node-Level Anomaly Detection in Industrial Control Systems
Lu, S., Wang, K., Liu, Y., & Wang, B. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3623299 · Cited by 4
“We measure the HMA’s functionality over 5 popular datasets in security research: the Secure Water Treatment dataset (SWaT) [55], the Critical Infrastructure Security Showdown dataset (CISS), the Water Distribution dataset (WADI: A water distribution testbed for research in the design of secure…”
Reconstruction-based Multi-Scale Anomaly Detection for Cyber-Physical Systems
Dong, Z., Liu, K., Han, D., Cao, Y., & Xia, Y. (2022) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iai55780.2022.9976844 · Cited by 5
“For example, in a water treatment system [9], changing the status of a valve in a pipe may affect pressure, flow rate, and other measurements.”
BackgroundTime-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection
Kang, J., Kim, M., Park, J., & Park, S. (2024) · IEEE Access · doi:10.1109/access.2024.3450709 · Cited by 7
“8(a) illustrates the results of seven different window sizes, W ∈ [6, 12, 24, 36, 48, 60, 72], indicating that the best F1 score is obtained for a window size of 12.”
BackgroundEnhancing Anomaly Detection in Maritime Operational IoT Time Series Data with Synthetic Outliers
Kim, H., & Joe, I. (2024) · Electronics · doi:10.3390/electronics13193912 · Cited by 8
“We used SWaT [27], WADI [28], SMAP [29], MSL [29], SMD [30], and IMOD datasets, as seen in Table 1.”
Guo, W., Wang, Y., Zhou, L., Jia, M., & Liu, Y. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.106871 · Cited by 4
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series
Liu, H., Qiu, X., Shi, Y., & Zang, Z. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890487 · Cited by 4
“…evaluations show that our model significantly improves performance (AUC and PR) compared to existing fault detection frameworks, particularly on multiple standard test benchmarks (SWaT [12], WADI [13]) and in operational scenarios, with lower false alarm rates and higher detection accuracies.”
Realigning Academic Cybersecurity Research With Industrial Needs in Cyber-Physical Systems
Verma, A. K., Navas-Fonseca, A., Burgos-Mellado, C., & Dragičević, T. (2025) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2025.3593689 · Cited by 4
“For example, the SWaT dataset [131] includes attack data, which is comprised of 41 attacks that were conducted over four days, as well as normal data streams that were gathered over seven days from 51 sensors and actuators.”
Huei, Y. C., Palleti, V. R., Silva, A., & POSKITT, C. M. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 4
Revitalizing Self-Organizing Map: Anomaly Detection Using Forecasting Error Patterns
Kim, Y. G., Yun, J.-H., Han, S., Kim, H. C., & Woo, S. S. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-78120-0_25 · Cited by 5
Bukharev, D. A., Николаевич, Р. А., & Sokolov, A. N. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-8759-4_51 · Cited by 4
Zhen, Z., Chen, Y., Segovia-Domínguez, I., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05936-0_40 · Cited by 4
Kuzmin, V., & Менисов, А. Б. (2022) · Информационно-управляющие системы · doi:10.31799/1684-8853-2022-4-29-43 · Cited by 4
Method for monitoring the state of elements of cyber-physical systems based on time series analysis
Semenov, V. V. (2022) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2022-22-6-1150-1158 · Cited by 4
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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