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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 101–200 of 734
Time-Series Aware Precision and Recall for Anomaly Detection
Hwang, W., Yun, J.-H., Kim, J., & Kim, H. C. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358118 · Cited by 46
“In addition, we use the SWaT dataset [1] consisting of time-series information obtained from a dedicated water plant testbed.”
MethodModelling cyber resilience in a water treatment and distribution system
Patriarca, R., Simone, F., & Gravio, G. D. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108653 · Cited by 43
Balla, A., Habaebi, M. H., Islam, M. R., & Mubarak, S. (2022) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2022.100532 · Cited by 48
NeuPot: A Neural Network-Based Honeypot for Detecting Cyber Threats in Industrial Control Systems
Shan, Y., Yao, Y. K., Zhao, T., & Yang, W. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3240739 · Cited by 42
“Dataset description and partitioning: We performed experiments on the Secure Water Treatment (SWaT) testbed dataset [33].”
MethodJing-yu, Y., Yue, Z., & Yuan, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109410 · Cited by 42
Contrastive Time-Series Anomaly Detection
Kim, H., Kim, S., Min, S., & Lee, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3335317 · Cited by 49
“We conducted experiments on four widely used public datasets: Secure Water Treatment (SWaT) [31], WAter DIstribu-tion (WADI) [32], Mars Science Laboratory rover (MSL) [33], and Soil Moisture Active Passive satellite (SMAP) [33].”
MethodA Comparison of ICS Datasets for Security Research Based on Attack Paths
Choi, S., Yun, J.-H., & Kim, S.-K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05849-4_12 · Cited by 42
Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks
Bernieri, G., Conti, M., & Turrin, F. (2019) · IEEE International Symposium on Measurements & Networking (M&N) · doi:10.1109/iwmn.2019.8805036 · Cited by 42
“In this work, we consider the SWaT testbed dataset [9] and, in particular, we will focus on the analysis of physical data.”
BackgroundMethodChen, D., Xie, Z., Liu, R., Yu, W. W. H., Hu, Q., Li, X., & Ding, S. X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3319468 · Cited by 41
Lian, Y., Geng, Y., & Tian, T. (2023) · Applied Sciences · doi:10.3390/app13031891 · Cited by 41
USMD: UnSupervised Misbehaviour Detection for Multi-Sensor Data
Alsaedi, A., Tari, Z., Mahmud, R., Moustafa, N., Mahmood, A. N., & Anwar, A. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3143493 · Cited by 44
“More information about the attacks and datasets can be found in ToN_IoT [47], SWaT [48], WADI [49] and gas pipeline [50].”
BackgroundMethodInfluentialCyberattack Detection Using Deep Generative Models with Variational Inference
Chandy, S. E., Rasekh, A., Barker, Z. A., & Shafiee, M. E. (2018) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001007 · Cited by 41
“…the availability of all labels and the possibility of conducting sensitivity and what-if analyses), future demonstrations on physical testbeds (Goh et al. 2016) or real-world systems are still necessary to account for the facets (e.g., the type variety of cyberattacks) not adequately captured…”
BackgroundWolsing, K., Wagner, E., Saillard, A., & Henze, M. (2022) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3545948.3545968 · Cited by 38
“TABOR was evaluated on the SWaT dataset [48], which was divided into smaller logically coherent units (models).”
BackgroundMethodInfluentialICS anomaly detection based on sensor patterns and actuator rules in spatiotemporal dependency
Cai, J., Wei, Z., & Luo, J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3393528 · Cited by 38
“The SWaT dataset [30] and the WADI dataset [31] used in the experiment were generated by iTrust, the Cyber Security Research Center at Singapore University of Technology and Design.”
MethodIntegrated management of safety and security in Seveso sites - sociotechnical perspectives
Ylönen, M., Tugnoli, A., Oliva, G., Heikkilä, J., Nissilä, M., Iaiani, M., Cozzani, V., Setola, R., Assenza, G., van der Beek, D., et al. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105741 · Cited by 41
Generating invariants using design and data-centric approaches for distributed attack detection
Umer, M., Mathur, A. P., Junejo, K. N., & Adepu, S. (2020) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2020.100341 · Cited by 36
Self-citationWen, M., Chen, Z., Xiong, Y., & Zhang, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129024 · Cited by 39
A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control SystemsPreprint
Feng, C., Li, T., Zhu, Z., & Chana, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06397 · Cited by 50
“Specifically, the SWaT testbed [41] is a scaled down water treatment plant which has a six-stage filtration process to purify raw water.”
BackgroundSUSAN: A Deep Learning based anomaly detection framework for sustainable industry
Gómez, Á. L. P., Maimó, L. F., Celdrán, A. H., & Clemente, F. J. G. (2023) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100842 · Cited by 34
LUAD: A lightweight unsupervised anomaly detection scheme for multivariate time series data
Fan, J., Liu, Z., Wu, H., Wu, J. X., Si, Z., Peng, H., & Luan, T. H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126644 · Cited by 34
Using GRU neural network for cyber-attack detection in automated process control systems
Lavrova, D. S., Петрович, З. Д., & Yarmak, A. V. (2019) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom.2019.8812818 · Cited by 34
“For experimental research, the SWaT testbed was taken, which is a smaller version of the automatic water treatment system [12].”
MethodSequence Mining Based Alarm Suppression
Dörgő, G., & Abonyi, J. (2018) · IEEE Access · doi:10.1109/access.2018.2797247 · Cited by 34
“Available: http://www.sciencedirect.com/science/ article/pii/B9780128009390000218 [3] E. Equipment, M. U. Association, and M. U. A. Staff, Alarm Systems: A Guide to Design, Management and Procurement (Engineering Equipment and Materials Users’ Association).”
MethodInfluentialAn explainable unsupervised anomaly detection framework for Industrial Internet of Things
Abudurexiti, Y., Han, G., Zhang, F., & Liu, L. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104130 · Cited by 40
Anomaly detection using invariant rules in Industrial Control Systems
Zhu, Q., Ding, Y., Jiang, J. S., & Yang, S. (2024) · Control Engineering Practice · doi:10.1016/j.conengprac.2024.106164 · Cited by 34
Applying of Generative Adversarial Networks for Anomaly Detection in Industrial Control Systems
Alabugin, S. K., & Sokolov, A. N. (2020) · Global Smart Industry Conference (GloSIC) · doi:10.1109/glosic50886.2020.9267878 · Cited by 33
Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems
Alguliyev, R. M., İmamverdiyev, Y., & Sukhostat, L. V. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05785-2 · Cited by 34
“Secure water treatment (SWaT) dataset was collected at the Singapore University of Technology and Design [19, 20].”
MethodOn the Performance of GRASP-Based Feature Selection for CPS Intrusion Detection
Quincozes, S. E., Mossé, D., Passos, D. G., Albuquerque, C. V. N., Ochi, L. S., & dos Santos, V. F. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3088763 · Cited by 33
MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks
Qi, P., Li, D., & Ng, S.-K. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00097 · Cited by 32
Automated detection-in-depth in industrial control systems
Jadidi, Z., Foo, E., Hussain, M., & Fidge, C. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08001-6 · Cited by 31
Sun, H., Huang, Y., Han, L., Fu, C., Liu, H., & Long, X. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103570 · Cited by 33
GAT-AD: Graph Attention Networks for contextual anomaly detection in network monitoring
Latif-Martínez, H., Suárez‐Varela, J., Cabellos‐Aparicio, A., & Barlet‐Ros, P. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110830 · Cited by 31
A Self-Tuning Cyber-Attacks’ Location Identification Approach for Critical Infrastructures
Alabassi, A., Jahromi, A. N., Karimipour, H., Dehghantanha, A., Siano, P., & Leung, H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3133361 · Cited by 31
“Detailed information on GP and SWaT SCADA models is available in [44] and [1], respectively.”
BackgroundInfluentialAdversarial Graph Neural Network for Multivariate Time Series Anomaly Detection
Zheng, B., Ming, L., Zeng, K., Zhou, M., Zhang, X., Ye, T., Yang, B., Zhou, X., & Jensen, C. S. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3419891 · Cited by 33
A hypergraph based Kohonen map for detecting intrusions over cyber–physical systems traffic
Jagtap, S. S., V. S., S. S., & V., S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.02.001 · Cited by 29
Ensemble Learning Methods: An Empirical Study
Sarmah, U., Borah, P., & Bhattacharyya, D. K. (2024) · SN Computer Science · doi:10.1007/s42979-024-03252-y · Cited by 29
From System Specification to Anomaly Detection (and back)
Fauri, D., dos Santos, D. R., Costante, E., et al. (2017) · doi:10.1145/3140241.3140250 · Cited by 29
Multi-step attack detection in industrial control systems using causal analysis
Jadidi, Z., Hagemann, J., & Quevedo, D. E. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103741 · Cited by 29
Anomaly Detection in Critical Infrastructure Using Probabilistic Neural Network
Raman, M. R. G., Somu, N., & Mathur, A. P. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-0871-4_10 · Cited by 29
Self-citationFung, C., Srinarasi, S., Lucas, K., Phee, H. B., & Bauer, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17143-7_24 · Cited by 32
“These datasets cover a variety of domains, including water distribution [4,33], water treatment [10], gas pipelines [24], and power generation [3,28].”
BackgroundMethodInfluentialAn improved autoencoder-based approach for anomaly detection in industrial control systems
Aslam, M. M., Tufail, A., Silva, L. C. D., Apong, R. A. A. H. M., & Namoun, A. (2024) · Systems Science & Control Engineering · doi:10.1080/21642583.2024.2334303 · Cited by 28
A real-time network based anomaly detection in industrial control systems
Zare, F., Mahmoudi-Nasr, P., & Yousefpour, R. (2024) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2024.100676 · Cited by 29
Securing Manufacturing Using Blockchain
Jadidi, Z., Dorri, A., Jurdak, R., & Fidge, C. (2020) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00262 · Cited by 28
“The dataset was generated by the iTrust cyber security research center [28].”
MethodUmer, M., Mathur, A. P., Junejo, K. N., & Adepu, S. (2017) · Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC) · doi:10.1145/3140241.3140248 · Cited by 29
Self-citationA novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)
Niu, Z., Guo, W., Xue, J., Wang, Y., Kong, Z., & Huang, L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103190 · Cited by 27
Adaptive Multivariate Time-Series Anomaly Detection
Lv, J., Wang, Y., & Chen, S. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103383 · Cited by 29
PLCDefender: Improving Remote Attestation Techniques for PLCs Using Physical Model
Salehi, M., & Bayat-Sarmadi, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3040237 · Cited by 27
“More information on the details of the SWaT testbed can be found in [13].”
Paredes, C. M., Martínez-Castro, D., Ibarra‐Junquera, V., & González, A. (2021) · Electronics · doi:10.3390/electronics10182238 · Cited by 28
“This dataset allows researchers to design and evaluate defense mechanisms for CPSs and contains both network traffic and data concerning the physical properties of the system [57].”
BackgroundMTAD: Multiobjective Transformer Network for Unsupervised Multisensor Anomaly Detection
Belay, M. A., Rasheed, A., & Rossi, P. S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3396690 · Cited by 31
“1) Secure Water Treatment (SWaT) Dataset [48], [49]: It is collected from a testbed that simulates the physical process and control system of a real-world water treatment system. end if ▷ Eq.”
BackgroundNarwariya, J., Malhotra, P., Tv, V., Vig, L., & Shroff, G. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16556 · Cited by 32
“However, in many practical applications such as a power plant or a water treatment plant (Goh et al. 2016), nodes typically have an associated type with more than one node belonging to the same type, e.”
BackgroundSemisupervised Training of Deep Generative Models for High-Dimensional Anomaly Detection
Xie, Q., Zhang, P., Yu, B., et al. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3095150 · Cited by 26
Federated Variational Learning for Anomaly Detection in Multivariate Time Series
Zhang, K., Jiang, Y., Seversky, L. M., Xu, C., Liu, D., & Song, H. (2021) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc51483.2021.9679367 · Cited by 30
“…is collected from a real-world water treatment testbed at the Singapore University of Technology and Design, representing a scaled-down version of a realistic Cyber-Physical System whose behaviors can be learned and analyzed in order to assure safety and security in a network environment [27].”
Background"Do you know existing accuracy metrics overrate time-series anomaly detections?"
Hwang, W., Yun, J.-H., Kim, J., & Min, B. G. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507024 · Cited by 28
“For the evaluation, we use two practical datasets, SWaT [4] and HAI [20], and three hypothetical datasets.”
MethodResultInfluentialChoubineh, A., Wood, D. A., & Choubineh, Z. (2020) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2020.100357 · Cited by 26
Gulzar, Q., & Mustafa, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-89650-5 · Cited by 29
Application of Artificial Immune Systems in Advanced Manufacturing
Pinto, R., & Gonçalves, G. (2022) · Array · doi:10.1016/j.array.2022.100238 · Cited by 29
Intrusion Detection System Based on In-Depth Understandings of Industrial Control Logic
Sun, M., Lai, Y., Wang, Y., Liu, J., Mao, B., & Gu, H. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3200363 · Cited by 24
“The detailed description of each dataset is as follows: 1) SWaT [24]: The SWaT dataset is collected in a six-stage safe water treatment testbed, which represents a scaled-down version of a real-world industrial water treatment plant.”
MethodScaphy: Detecting Modern ICS Attacks by Correlating Behaviors in SCADA and PHYsical
Ike, M., Phan, K., Sadoski, K., Valme, R., & Lee, W. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179411 · Cited by 24
“We replicated the FL incident with realistic water treatment scenario using FactoryIO and open-source data [26, 27].”
BackgroundMethodMultivariate Time Series Anomaly Detection via Low-Rank and Sparse Decomposition
Belay, M. A., Rasheed, A., & Rossi, P. S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3452318 · Cited by 28
“We conducted our experiments using two publicly available multivariate datasets from testbeds of multisensor systems: Secure Water Treatment (SWAT) [38], [39] and Water Distribution (WADI) [40].”
MethodPrecursor-of-Anomaly Detection for Irregular Time Series
Jhin, S. Y., Lee, J., & Park, N. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599469 · Cited by 24
Convolutional Neural Networks Based Detection System for Cyber-attacks in Industrial Control Systems
Bhuyan, M. K., Kamruzzaman, M., Nilima, S. I., et al. (2024) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2024.6.3.9 · Cited by 23
“One notable dataset is the Secure Water Treatment (SWaT) dataset, which simulates a real-world water treatment plant and includes a variety of cyber-attack scenarios (Goh et al., 2016).”
BackgroundA Reference Architecture for IIoT and Industrial Control Systems Testbeds
Craggs, B., Rashid, A., Hankin, C., Antrobus, R., Serban, O., & Thapen, N. (2019) · Living in the Internet of Things (IoT 2019) · doi:10.1049/cp.2019.0169 · Cited by 23
“1 Power System † ARFF MODBUS [9] N · 2 (normal vs attack) 128 6 · 3 (normal vs natural vs attack) · 41 (attack scenarios) 2 Gas Pipeline † CSV MODBUS [10] Y 2 (normal vs attack) 12 3 Gas Pipeline and Water Storage Tank † ARFF MODBUS [11] Y 8 (attack scenarios) 28 4 New Gas Pipeline † ARFF MODBUS [12] Y 8 (attack scena…”
BackgroundUnmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry
Herrmann, L., Bieber, M., Verhagen, W. J. C., Cosson, F., & Santos, B. F. (2024) · CEAS Space Journal · doi:10.1007/s12567-023-00529-5 · Cited by 25
“…during the Table 1 Performance comparison of recent deep anomaly detection algorithms Server Machine Dataset (SMD) [26], Mars Science Laboratory (MSL) and Small Active Passive Satellite (SMAP) [14], Secure Water Treatment (SWAT) [27] Name Year Network type Anomaly criterion F 1PA Benchmark results”
MulGad: Multi-granularity contrastive learning for multivariate time series anomaly detection
Xiao, B.-W., Xing, H.-J., & Li, C.-G. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103008 · Cited by 24
FALCON: Framework for Anomaly Detection in Industrial Control Systems
Sapkota, S., Mehdy, A. K. M. N., Reese, S. J., & Mehrpouyan, H. (2020) · Electronics · doi:10.3390/electronics9081192 · Cited by 22
Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection
He, Y., Bian, Y., Ding, X. L., Wu, B., Guan, J., Zhang, J., & Zhou, S. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3663573 · Cited by 21
“(1) Industrial water treatment system consists of two datasets: SWaT dataset [13] (2) Spacecraft monitoring system includes two datasets collected by NASA [14]: SMAP satellite and MSL rover.”
HLMCC: A Hybrid Learning Anomaly Detection Model for Unlabeled Data in Internet of Things
Alghanmi, N., Alotaibi, R., & Buhari, S. M. (2019) · IEEE Access · doi:10.1109/access.2019.2959739 · Cited by 22
“called the Secure Water Treatment (SWaT) dataset [27], [28].”
BackgroundDistributed Architecture of an Intrusion Detection System in Industrial Control Systems
Abid, A., Jemili, F., & Korbaa, O. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-16210-7_39 · Cited by 21
DUMA: Dual Mask for Multivariate Time Series Anomaly Detection
Pan, J., Ji, W., Zhong, B., Wang, P., Wang, X., & Chen, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3225338 · Cited by 24
“In a CPS, the relationships between sensors are complex and intricate.”
BackgroundInfluentialGraph-guided masked autoencoder for process anomaly detection
Jia, M., Xu, D., Yang, T., et al. (2024) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2024.04.052 · Cited by 21
Active fuzzing for testing and securing cyber-physical systems
Chen, Y., Xuan, B., POSKITT, C. M., Sun, J., & Zhang, F. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 21
Deep Federated Learning-Based Cyber-Attack Detection in Industrial Control Systems
Jahromi, A. N., Karimipour, H., & Dehghantanha, A. (2021) · Conference on Privacy, Security and Trust · doi:10.1109/pst52912.2021.9647838 · Cited by 23
Quincozes, S. E., Passos, D. G., Albuquerque, C. V. N., Mossé, D., & Ochi, L. S. (2022) · Annals of Telecommunications · doi:10.1007/s12243-022-00912-z · Cited by 21
“We use two datasets that can represent the perception layer from a CPS: (i) Secure Water Treatment (SWaT [10]) is a water treatment dataset for cybersecurity research; and (ii) WSN-DS dataset [3], which is based on a wireless sensor network.”
BackgroundMethodInfluentialZhang, R., Chen, J., Song, Y., Shan, W., Chen, P., & Xia, Y. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02204-9 · Cited by 20
“They are Secure Water Treatment SWaT [32], Skoltech Anomaly Benchmark SKAB [33], Mars Science Laboratory rover MSL [34], Soil Moisture Active and Passive SMAP [35].”
ACWA: an AI-driven cyber-physical testbed for intelligent water systems
Batarseh, F. A., Kulkarni, A., Sreng, C., Lin, J., & Maksud, S. (2023) · Water Practice & Technology · doi:10.2166/wpt.2023.197 · Cited by 21
The voraus-AD Dataset for Anomaly Detection in Robot Applications
Brockmann, J. T., Rudolph, M., Rosenhahn, B., & Wandt, B. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3332224 · Cited by 31
“Hyperparameters were taken from their experiments with SWaT [18] since the task and dataset are similar to our scenario.”
MethodGao, C., Ma, H., Pei, Q., & Chen, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06412-6 · Cited by 20
“The SWaT dataset [34] is a scaled-down version of a real-world industrial water treatment plant simulated in a laboratory setting that integrates digital and physical elements to control and monitor system behavior.”
Deep anomaly detection for time series: A survey
Jia, X., Peng, X., Peng, W., Zhao, B., Li, H., & Shen, C. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100787 · Cited by 27
Elnour, M., Meskin, N., & Khan, K. M. (2020) · IEEE Conference on Control Technology and Applications (CCTA) · doi:10.1109/ccta41146.2020.9206394 · Cited by 19
“The details of the attack models and scenarios are provided in [18].”
BackgroundLearning-guided network fuzzing for testing cyber-physical system defences
Chen, Y., POSKITT, C. M., Sun, J., Adepu, S., & Zhang, F. (2019) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 19
Self-citationKumar, A., Saxena, N., Jung, S., & Choi, B. J. (2021) · Energies · doi:10.3390/en15010212 · Cited by 19
“Our work uses a smart power system [10] and a water treatment plant [11] as case studies.”
BackgroundMethodMiao, Q., Xu, C., Zhan, J., Zhu, D., & Wu, C. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1645-0_42 · Cited by 19
Kongsorot, Y., Musikawan, P., Aimtongkham, P., et al. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3303422 · Cited by 19
“ICS data: gas pipeline (GP) [46], [47] and secure water treatment (SWaT) [48].”
BackgroundMethodAttributions for ML-based ICS Anomaly Detection: From Theory to Practice
Fung, C., Zeng, E., & Bauer, L. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23216 · Cited by 19
“Despite the wide range of potential attack strategies, public ICS datasets predominantly contain high-magnitude, constant-valued manipulations [7], [27], and prior work evaluates attributions on only a small number of attacks from these datasets [34], [39].”
BackgroundMethodInfluentialLiu, J. L., Huang, Y., Wu, D., Yang, Y., Chen, Y., Chen, L., & Zhang, Y. (2024) · Sensors · doi:10.3390/s24165316 · Cited by 21
“SWaT (Secure Water Treatment) [22] is essential for research in the design of secure Cyber–Physical Systems.”
BackgroundMethodFan, Y., Fu, T., Листопад, Н. И., Liu, P., Garg, S., & Hassan, M. M. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.08.048 · Cited by 18
Sajjadi, P., Dinmohammadi, F., & Shafiee, M. (2025) · Sensors · doi:10.3390/s25134164 · Cited by 18
“The proposed models will be evaluated using the Secure Water Treatment (SWaT) dataset, which is derived from a scaled-down industrial water treatment testbed [21].”
Code integrity attestation for PLCs using black box neural network predictions
Chen, Y., POSKITT, C. M., & Sun, J. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468617 · Cited by 18
“These include anomaly detectors, typically trained on the publicly released dataset [1, 41] using unsupervised learning techniques, e.”
BackgroundMethodInfluentialMitigating Adversarial Attacks on Data-Driven Invariant Checkers for Cyber-Physical Systems
Maiti, R. R., Huei, Y. C., Palleti, V. R., Silva, A., & POSKITT, C. M. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3194089 · Cited by 18
“SWaT is able to produce up to five [49], [50].”
BackgroundPractical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems
Kravchik, M., Demetrio, L., Biggio, B., & Shabtai, A. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102901 · Cited by 18
“The synthetic data and the static data collected from the SWaT testbed provide a good starting point for assessing the effectiveness of the proposed poisoning algorithms.”
MethodInfluentialA Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems
Bahadoripour, S., MacDonald, M. E., & Karimipour, H. (2023) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58465.2023.10143147 · Cited by 18
“To evaluate the proposed model, the SWaT dataset [30], which includes both network and sensor modalities, was used.”
MethodKalmanAE: Deep Embedding Optimized Kalman Filter for Time Series Anomaly Detection
Huang, X., Zhang, F., Wang, R., Lin, X., Liu, H., & Fan, H. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3329098 · Cited by 19
“…seven datasets with different categories of time series from the UCR Archive [42], a real-world bearing dataset, CWRU, an arrhythmia ECG dataset, MIT-BIH [43], a water treatment dataset, SWaT [44], and a simulated dataset, Waveform [45], to evaluate the effectiveness of the proposed method.”
Madhawa, S., Balakrishnan, P., & Umamakeswari, A. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169946 · Cited by 18
“…of SDN, the simulation of which is carried out in Mininet [10], a Linux based open source network emulator for SDN. Real-time multivariate dataset [11] comprising both the normal and data integrity attacks from a water processing test bed obtained from iTrust, a research Centre is materialized in…”
A Modular Hybrid Learning Approach for Black-Box Security Testing of CPS
Castellanos, J. H., & Zhou, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21568-2_10 · Cited by 18
Runtime Enforcement for Control System Security
Ruggero Lanotte, A., Merro, M., & Munteanu, A. (2020) · Proceedings - Computer Security Foundations Workshop/Proceedings · doi:10.1109/csf49147.2020.00025 · Cited by 18
“The SWaT system has been used to provide a dataset containing physical and network data recorded during 11 days of activity [30].”
MethodDeriving invariant checkers for critical infrastructure using axiomatic design principles
Huei, Y. C., Palleti, V. R., Maiti, R. R., Silva, A., & POSKITT, C. M. (2021) · Cybersecurity · doi:10.1186/s42400-021-00069-7 · Cited by 18
“A SWaT dataset is available, consisting of all the data recorded by this server over a period of several days, including a few during which the testbed was subjected to attacks (iTrust Labs: Datasets 2020; Goh et al. 2016).”
MethodAn ensemble approach to anomaly detection using high- and low-variance principal components
Moon, J.-H., Yu, J.-H., & Sohn, K.-A. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107773 · Cited by 18
Recent Advances in Machine Learning-based Anomaly Detection for Industrial Control Networks
Wang, Q., Chen, H. H., Li, Y., & Vucetic, B. S. (2019) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iciai.2019.8850828 · Cited by 18
“The framework has been tested on the Secure Water Treatment dataset [17], which”
MethodTime series anomaly detection via temporal relationship graphs and adaptive smoothing
Ma, R., Ma, Y., & Liu, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113298 · Cited by 20
Zhang, S., Liu, S., Sun, J., Chen, Y., Huang, W., Liu, J., Liu, J., & Hao, J. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678832 · Cited by 17
“These unsafe states have been reported in an established benchmark of SWAT network attacks [34] which was manually crafted by experts.”
BackgroundMachine Learning-Based Security Solutions for Critical Cyber-Physical Systems
Raza, A., Memon, S. A., Nizamani, M. A., & Shah, M. H. (2022) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs55398.2022.9800811 · Cited by 21
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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