The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
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 601–700 of 734
Bukharev, D. A., & Sokolov, A. N. (2025) · International Russian Smart Industry Conference (SmartIndustryCon) · doi:10.1109/smartindustrycon65166.2025.10985971 · Cited by 1
“The dataset used for this study, employing hierarchical clustering analysis methods, is the Secure Water Treatment (SWaT) dataset [20].”
Artefact Provenance Graphs for Anomaly Inference in Industrial Control Systems
Cook, M. M., & Pezaros, D. P. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-92882-6_10
“Most existing approaches are evaluated using the SWaT dataset [11] which includes both network and sensor measurement data extracted from the physical process.”
Jin, Y., Liu, X., Hu, B., Walker, J., Wang, K., Wu, W., & Zhong, T. (2025) · Electronics · doi:10.3390/electronics14102068
“However, as both systems require 5–6 h to stabilize [32], we excluded the initial 21,600 unstable data points generated during this period.”
A survey of ML Resources for IEC 61850 in Power Grid Security
Baranwal, K., & Dagale, H. (2025) · doi:10.1109/ids66066.2025.00009
Multivariate Time-Series Data Anomaly Detection via Dimension Independence and Reconstruction
Hu, W., Xu, J., & Zhao, X. (2025) · Data Storage and Data Engineering · doi:10.1145/3725472.3725475
“(4) SWaT [6] is an IoT water treatment monitoring system dataset, where anomalies are manually generated network attacks.”
AnomalyTK: A Multivariate Time Series Anomaly Detection Method
Xiao, T., & Zeng, F. (2025) · International Conference on Sensor-Cloud and Edge Computing System (SCECS) · doi:10.1109/scecs65243.2025.11065321
“We used the SWaT dataset [13] and the WADI dataset [14] to verify the effectiveness of our model.”
TGM: An Industrial Sensor Network Anomaly Detection
Wang, T., Zou, C., Liu, Y., Zhou, L., & Wang, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9946-9_34
G-DCAP: Stealthy Adversarial Attacks on Multi-Sensor IoT Systems
Gunawardena, R., Karunanayake, N., Seneviratne, S., Masood, R., & Kanhere, S. S. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00028 · Cited by 1
“The SWaT [29] and WADI [30] datasets are based on water treatment physical test-bed systems that simulate a small-scale modern CPS.”
Attacks against industrial control systems in wireless networks
Azeez, M., Katzenbeisser, S., & Gelóczi, E. (2026) · Computers & Security · doi:10.1016/j.cose.2026.105107
Anomaly Detection for the Industrial Control Systems: A Hybrid Deep Learning Approach
Tebeje, M. W., Munaye, Y. Y., & Birihanu, E. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-6034-6_41
Temporal Harmonization of Heterogeneous Software Logs: A Unified Model for Time-Series Analysis
Abdullah, M., Hnětynka, P., & Mahmud, J. S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04403-7_29
Automated Identification of ICS Topology and Device Types via Protocol-Agnostic Passive Monitoring
Heo, W., & Shin, D. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3623634
Hybrid-Quantum Machine Learning Approach for Anomaly Detection in Complex Industrial Systems
Senese, A., Esposito, E., Vito, S. D., et al. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-08271-8_65
Edge-AD: An Edge-Oriented Anomaly Detection Approach with Gated Fusion and Dynamic Thresholding
Wang, C., Wang, T., Chu, Y., Yang, X., & Su, Z. (2025) · International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation (IoTAIMA) · doi:10.1109/iotaima66468.2025.11212741
“…the universality and practicability of the method, two typical industrial anomaly detection datasets were selected: SWaT (Secure Water Treatment) [6]: containing various injection attack scenarios; WADI (Water Distribution) [7]: simulating the complex anomalies in the water distribution system…”
Sun, W., Yu, R., Li, X., Zhao, M., Zhou, C., Jiang, F., & Ma, J. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425500405
Homaei, M., Tarif, M., Rodríguez, P. G., Lindo, A. C., & Avila, M. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5735701
Adaptive Hierarchical Attention for Multivariate Time Series Anomaly Detection
You, X., Guo, X., Wang, B., & Wang, C. (2025) · Annals of Data Science · doi:10.1007/s40745-025-00662-4
Zhang, X.-M., Zheng, Q., Hao, R., Xia, Z., Zhou, J., & Xiang, J. (2025) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre66568.2025.00017
“Compared with other ICS datasets (e.g., Gas Pipeline [33], SWaT [34], Edge-IIoTSet [35]), we choose the WDT dataset for three main reasons: (1) It provides raw network traffic in pcap files with clear labels for each packet; (2) It originates from a realistic ICS environment using the widely…”
(2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228430
“• The Secure Water Treatment(SWaT) [22] dataset is obtained from a secure water processing system, which is a single-entity dataset with 55 variates, documenting seven days of normal and four days of abnormal operations.”
Industrial anomaly detection via curriculum-based deep learning
Dube, D. C., & Akar, M. (2025) · Applied Intelligence · doi:10.1007/s10489-025-07032-w
“Secure Water Treatment (SWaT), which consists of cyber and physical attacks, is a popular dataset in MVTS anomaly detection [45, 46 ].”
Invariant Rules-Based Anomaly Detection in Industrial Control Systems
Liu, K., Xie, Y., Xie, S., Wang, X., Wang, H., Li, Z., Pan, Z., & Sun, L. (2025) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana66698.2025.00013
“According to [18], [20], the system typically requires approximately five hours to reach a stable operational state after startup.”
SAAT: Stealthy Adversarial Attack on IDS in Cyber Physical Systems Using Control Logic Induction
Pandey, R. K., & Das, T. K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-13714-2_19
Abdullahi, B., & Vadera, S. (2025) · International Conference on Computer Vision and Data Mining (ICCVDM) · doi:10.1109/iccvdm66874.2025.11290063
“Despite their advantages, these hybrid models often struggle to balance short-term fluctuations and long-term trends, which are critical for identifying slow-developing faults[9, 19-22].”
Cybersecurity in the Water Economy
Imamoğlu, M. B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1777-9.ch004
Mahmud, J. S., Farou, Z., & Lendák, I. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02204-0
SAGE: Spatiotemporal Feature Fusion for Anomaly Detection in Multivariate Time Series
Shi, H., Wang, Y., & Zhou, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6203-9_8
Zhang, Y., Liu, J. L., Wang, Z., Zhang, H., Chen, L., & Chen, Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3651780
“However, in industrial timing anomaly detection, many dataset (e.g., secure water treatment (SWaT) [20]) contains only a small number of unclassified anomaly labels [21].”
Fourier Latent Transformer for Anomaly Signal with High-Frequency Reconstruction
Chalongvorachai, T., & Woraratpanya, K. (2025) · doi:10.1109/icitee66631.2025.11338305
“Two datasets are used to evaluate the proposed method: (i) a synthetic dataset with high-frequency characteristics; and (ii) the Secure Water Treatment (SWaT) dataset [2].”
Gu, X., Sun, Y., Han, A., Bi, X. J., Bi, K., Ying, S., & Ji, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5716-5_42
PRAD-Net: Periodic Reorganization Anomaly Detection Network using Anomaly Injection Strategy
Lu, H., Li, X., & Ning, Z. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342509
“The descriptions of these datasets are as follows: • SWaT (Secure Water Treatment) [30]: Collected over 11 days from an industrial water treatment system with 51 sensors, including 7 days of normal operation and 4 days with physical attacks.”
S-DGAT: A Spatially Enhanced Dynamic Graph Attention Network for Multi-Level Anomaly Detection
Lu, Y., Chen, Z., Xu, Y., Gu, S., & Jin, X. (2026) · IEEE Access · doi:10.1109/access.2026.3658935
“This study utilizes four representative real-world datasets: WADI [54], SWAT [55], FT-AED [56], and SKAB [57].”
Zhang, Y., Zhang, S., Wei, W., & Lai, Y. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342651
Developing a Strong CPS Defender: An Evolutionary Approach
Hu, Q., POSKITT, C. M., Sun, J., & Chen, Y. (2025) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1109/raid67961.2025.00028
“Collecting data from scenarios involving active attacks or abnormal behavior—such as those in [10], [11]— requires significant time, effort, and domain expertise.”
Dhali, S., Ghosh, K., & Duttagupta, S. P. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00111
“Communication between sensors, actuators, and PLCs is handled through both wired and wireless connections [27] [28].”
Hybrid TCDF with Knowledge Graph Verification for Temporal Causal Graphs in Industrial Systems
Nguyen, V. M., Le, V. T. A., & Le, C. H. T. (2025) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf68649.2025.11365038
“To evaluate the proposed hybrid TCDF-KG framework, we conducted experiments on the SWaT (Secure Water Treatment) dataset [17], a publicly available multivariate time series dataset collected from a real-world water treatment testbed at the Singapore University of Technology and Design (SUTD).”
Noh, J., Lee, J., Oh, S., Park, J., Hahn, M., & Kim, J. (2026) · Journal of Digital Contents Society · doi:10.9728/dcs.2026.27.1.187
Kong, J., Gu, W., Jiang, M., & Tao, X. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-02990-0
“● Secure Water Treatment(SWaT) dataset [7].”
N41: Leveraging One Target and Neighboring Features to Improve Anomaly Detection Accuracy
Hwang, W.-S., Yun, J.-H., & Min, B. G. (2026) · doi:10.1145/3773966.3779387
IMADP: Imputation-Based Anomaly Detection in SCADA Systems via Adversarial Diffusion Process
Li, W., Yao, Y., Zhang, N., Sheng, C., Ran, Z., & Yang, W. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3670062
“Secure Water Treatment : The SWaT (S) [40] originates from a scaled water treatment facility, capturing 11 days of operational data (7 days normal operation, 4 days with 36 simulated attacks) across 51 sensors, yielding an 11.98% anomaly ratio.”
InfluentialLCDTwin: LLM-Driven Contextual Digital Twin for Actuator-Sensor Correlation in ICS
Kim, Y., Heo, W., Woo, E., & Shin, D. (2025) · International Conference on Robotics, Automation and Artificial Intelligence (RAAI) · doi:10.1109/raai67517.2025.11423321
“LCDTwin is evaluated using the Secure Water Treatment (SWaT) dataset [9], a standard benchmark for ICS anomaly detection.”
SC-GAT: A Stabilized Causal Graph Attention Network for Detecting Attacks in Water Treatment Plant
Ruckcharti, U., & Arpanickkanondt, C. (2026) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst67832.2026.11432395
“The release of the Secure Water Treatment (SWaT) dataset by Goh et al. [1] was an important step, as it gave researchers a high-quality dataset to test on.”
Dhali, S., Ghosh, K., & Duttagupta, S. P. (2025) · IEEE International Conference on Emerging Trends in Computing and Communication (ETCOM) · doi:10.1109/etcom66606.2025.11436417
“[28] [29] Fig.”
A Novel Dynamic Spatio-Temporal Collaborative Model for Multivariate Time Series Anomaly Detection
Yin, J., Bian, A., Liu, K., Chen, P., Li, X., & Chang, Y. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-21171-2_26
Scalable and flexible density estimation for complex data distributions
Haxholli, E. (2023) · theses.fr (ABES) · doi:10.70675/a91968b0z4150z4d2az9434z27b7bfd4a076
Trustable machine learning for intrusion detection systems
Duraz, R. (2024) · theses.fr (ABES) · doi:10.70675/e2fe5f32z19e5z41caz9b55z7b33b874c3c0 · Cited by 1
Benefit From Noise: Detecting Time-Series Anomaly by Distinguishing Prior and Posterior Noises
Xing, S., Niu, J., Ren, T., & Rodrigues, J. J. P. C. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3682883
“We evaluate NoiseAD using five benchmark datasets derived from diverse real-world applications, details of which could be found in Table I. (1) SWaT (secure water treatment) is obtained from 51 sensors of the water treatment system under continuous operations [56].”
Han, T., Li, Y., Xiong, Q., Zheng, S., & Guo, J. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463550
Bipolar Relational Network for Irregular Time Series Anomaly Detection
Zhang, X., Lu, X., Li, P., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464673
Barapatre, H., Patil, H., Kamle, A., et al. (2026) · International Journal of Engineering Technologies and Management Research · doi:10.29121/ijetmr.v13.i4.2026.1769
Raza, A., & Memon, S. (2026) · Global Conference on Wireless and Optical Technologies (GCWOT) · doi:10.1109/gcwot69191.2026.11499741
Federated Quantum Kernel Learning for Decentralized Anomaly Detection in Zero Trust Edge Networks
Xu, S., & Qian, Y. (2026) · International Conference on Quantum Communications, Networking, and Computing (QCNC) · doi:10.1109/qcnc69040.2026.00161
“While real network traffic datasets (e.g., CIC-IDS2017 [11] and SWaT [12]) would demonstrate operational applicability, we use synthetic data as a principled first step to validate the framework’s theoretical properties before introducing the confounding complexity of real-world feature distributions.”
MS-STNet: Multi-scale Spatio-Temporal Modeling for Multivariate Time Series Anomaly Detection
Wang, M., Li, J. F., Cui, Y., Li, F., Zhang, J. Z., & Wang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-0372-7_37
Li, Z., Tong, J., & Liu, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3694131
“The evaluation uses four widely-adopted intrusion detection datasets: NSL-KDD [40], CICIDS2017 [41], UNSW-NB15 [42], and SWaT (Secure Water Treatment) [43].”
Causality-Driven Test Case Minimisation for Cyber-Physical Systems
Foster, M., POSKITT, C. M., Latimer, N. R., Walkinshaw, N., Somers, R. J., & Hierons, R. M. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3816435
“This prevents the need to put real patients at risk, but is still rather slow and computationally intensive.”
Ghosh, M., Meshram, A., Karch, M., Haas, C., Zhang, X., & Singh, M. (2026) · ACM Workshop on Cyber-Physical System Security (CPSS) · doi:10.1145/3775042.3807884
“Several publicly available datasets have been developed to support research in ICS security, including SWaT [18], WADI [1], BATADAL [50], Gas Pipeline [38], and Power Grid [40] datasets.”
Almufti, S. M. (2025) · Journal of Artificial Intelligence Machine Learning and Neural Network · doi:10.55529/jaimlnn.52.48.57
Edge AI-based Anomaly Behavior Analysis for Industrial Control Systems
Yang, Z., Shi, Q., Lin, Y.-Z., Salehi, S., Satam, P., & Shao, S. (2026) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed69900.2026.11534670
“Anomaly detection is a practical building block for ICS security because it can surface abnormal behavior arising from faults, misconfigurations, or cyberattacks without relying on prior knowledge of specific attack signatures [10], [11].”
Cai, X., Sun, Y., Chen, Y., et al. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3698054
“To assess resilience under intelligent adversarial interference, real-world attack traces from the SWaT dataset [29] are injectedintothefeedbackchannel.”
Asaad, R. R. (2025) · Journal of Artificial Intelligence Machine Learning and Neural Network · doi:10.55529/jaimlnn.52.79.87
Chilukury, P. K., Reddy, S. R. B., Tirupati, K. K., & Methuku, N. K. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541767
“Cyber-physical systems (CPS) are increasingly relying on continuous streams of sensor data to ensure their safe, resilient and secure operation, which means that the detection of anomalies becomes a fundamental requirement in the modern industrial monitoring environment [1], [2].”
An Integrated Testbed for MITRE-Mapped Attack Emulation in Industrial Control Networks
Rahmani, J., Detken, K. O., & Sikora, A. (2026) · Sensors · doi:10.3390/s26113514
“…multi-protocol or multi-process frameworks (CPGrid-OT [10] on DNP3, ICSSIM [11] with virtualised processes, Noorizadeh et al. [12] for chemical false-data injection), and multi-stage attack benchmarks (SWaT and WADI [13] for water treatment, CIC-APT-IIoT [14] for provenance-based APT traces).”
Emergent detection of concept drift within the glia-inspired ‘rhythmic sharing’ algorithm
Whitehouse, I., Kang, H., & Losert, W. (2026) · npj Unconventional Computing · doi:10.1038/s44335-026-00067-3
“The highlight of our results is on the Secure Water Treatment testbed (SWaT) dataset and water distribution testbed (WADI) dataset 13,14 .”
Kang, R., Zhang, Z., Zhang, X., Chen, J. C., Xu, R., Zhang, Y., & Rao, Z. (2026) · Entropy · doi:10.3390/e28060635
“…CVSS exploitability scores as proxy variables for estimating attack success probability; alternatively, by repeatedly executing specific attack–defense actions in highly realistic ICS testbeds [32,33], the frequency of state transitions can be statistically obtained to estimate their probability.”
DMSA: a dual-mask spatiatemporal autoencoder for multivariate time series anomaly detection
Bao, Y. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07310-1
“Secure Water Treatment Dataset (SWaT) [41, 42 ]”
Explainable Reinforcement Learning for Automated Incident Response in Critical Infrastructure
Alauthman, M., Almomani, A., Al–Qerem, A., et al. (2026) · doi:10.1109/icciaa68481.2026.11543556
“We recommend three datasets commonly used for industrial security research: • SWaT (Secure Water Treatment): multiday operation of a six-stage water treatment testbed under normal and attacked modes [23], [24].”
A 492.8-TOPS/W STT-MRAM Sparsity-Adaptive Compute-in-Memory Macro for Edge AI Inference
Cui, J.-L., Shi, T., Wang, S., Zhu, H., Du, H., Cui, Y., Zhou, Y., Fu, Z., Si, X., Liu, B., et al. (2026) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2026.3698324
“[38], [39]; and 3) four-layer VAE, FC(4, 64)–FC(64, 32)–FC(16, 64)–FC(64, 4) with 3748 parameters.”
Multi-Scale Spatiotemporal Dependency Learning for Anomaly Detection in Multivariate Time Series
Duan, S., Xin, L., Liu, Y., & Zhang, D. (2026) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11542860
“To evaluate the performance of MS-STDL, we conduct experiments on five widely used datasets, including MSL [8], SMAP [8], PSM [9], SMD [10], and SWaT [11], which cover diverse application domains.”
Kellerer, N., & Hagenmeyer, V. (2026) · ACM Sustainability Week · doi:10.1145/3765611.3815147 · Cited by 1
Fu, C., Besati, S., & Wang, M. (2026) · doi:10.1145/3765611.3815146
Lui, P. H., Siqueira, L. P., Kazienko, J. F., et al. (2026) · doi:10.5753/sbcas.2026.21576
“For example, cyber-physical system datasets such as the Secure Water Treatment (SWaT) testbed integrate network traffic with physical process measurements to study attacks affecting both communication and system behavior [Goh et al. 2016].”
Revolutionizing Cyber-Physical Security: AI-Driven Anomaly Detection in Blockchain Ecosystems
Nair, R. R., Babu, T. L., Almheiri, M., et al. (2026) · doi:10.1109/intersys69825.2026.11565243
“The experiments were conducted using the Secure Water Treatment (SWaT) dataset, a well-established benchmark for anomaly detection in industrial cyber-physical systems [14].”
Unsupervised Anomaly Detection Framework for Multimodal Data in Industrial Control Systems
Kim, Y., An, G., Kim, K., & Ha, J. (2026) · Sensors · doi:10.3390/s26123914
Multi-Scale Spatiotemporal Dependency Learning for Anomaly Detection in Multivariate Time Series
Duan, S., Xin, L., Liu, Y., & Zhang, D. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567117
“To evaluate the performance of MS-STDL, we conduct experiments on five widely used datasets, including MSL [8], SMAP [8], PSM [9], SMD [10], and SWaT [11], which cover diverse application domains.”
A Comparative Study of Large Language Models for Industrial Cyber-Physical Security
de Curtò, J., de Zarzà, I., Cano, J., & Calafate, C. T. (2026) · Electronics · doi:10.3390/electronics15132779
ATOM: Adaptive Temporal Omniview Mixer for Lightweight Multivariate Time-Series Anomaly Detection
Jia, X., Peng, W., Xun, P., Zhao, B., & Shen, C. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3702381
“[22], Soil Moisture Active Passive (SMAP) and Mars Science Lab (MSL) [23], Secure Water Treatment (SWaT) [24], andWater Distribution(WADI) [25].”
He, Y., Ye, R., & Zhang, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3708675
“1) SWaT: The real-world water treatment dataset (e.g., the SWaT dataset [70]) is used.”
Jlidi, A., Benotsmane, R., & László, K. (2026) · International Carpathian Control Conference · doi:10.1109/iccc71363.2026.11593213
“In cyber-physical security research, SWaT and WADI have become standard benchmarks for anomaly detection in industrial control systems [1], [8].”
Xu, G., Ye, Z., Huang, X., Liu, L., Wu, C., & Mumtaz, S. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3710784
“Following standard practice, we use the o ffi cial train / test splits and the corresponding public evaluation protocols for all datasets [5], [25], [26], [27].”
Noor, M. M., & Hussain, N. A. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2888-1_18
Intrusion Behavior Profiling in Smart Grid Infrastructure Using Swin Transformer Architecture
Thiyab, M. N., Ali, M. S., Hamudi, N. A., et al. (2026) · doi:10.1109/isades69945.2026.11608196
Spatio-Temporal Multi-Sensor Anomaly Detection With Concurrent-Noise Distinction
Xiao, H., & Yuan, J. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3718334
“We evaluate STAND on three public multivariate time series anomaly detection benchmarks: MSL [20], SWaT [21], [22], SMD [23]. and a high-noise dataset AETA (multi-component seismic monitoring system) [24] for ablation and robustness studies.”
AI Based Traffic Congestion Monitoring and Alert System
N, S. M., Janya, C., K, M. T., et al. (2026) · doi:10.1109/icdca69396.2026.11620557
“Conversely, although Goh et al. (2017) concentrate on critical infrastructure datasets, they indirectly emphasize the need to have reliable and well-structured data to design intelligent systems.”
Dobrády, Z., Nagy, S., Oren, Y., Routtenberg, T., & Puzis, R. (2026) · International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) · doi:10.1109/icest71230.2026.11623646
“The Secure Water Treatment (SWaT) dataset [15] is used here as the basis for the PLC-side timing evaluation.”
Wang, Y., Han, F., Li, K., Liu, S., Chen, T., & Peng, Z. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817943
Local anomaly detection via subgraph embedding on multivariate data streams
Li, G., & Jung, J. K. (2026) · Discover Computing · doi:10.1007/s10791-026-10441-1
“The industrial datasets included the Secure Water Treatment (SWaT) system [21], the Server Machine Dataset (SMD) [22], the Soil Moisture Active Passive (SMAPE) dataset [22], and data from the Mars Science Laboratory rover (MSL) [23].”
Yang, C., Dong, X., Hao, J., et al. (2026) · Algorithms · doi:10.3390/a19080662
Xie, F., Zhong, H., Chen, Q., Qiu, K., Lu, W., Wang, X., & Wang, X. (2026) · International Conference on Electronic Engineering and Informatics (EEI) · doi:10.1109/eei70303.2026.11640497
“Industrial datasets such as SWaT [10], [11] and SKAB [12] make process-aware attack detection reproducible, while CPS-security surveys and resilience studies emphasize that model-free detectors must be evaluated carefully under realistic protocols [1].”
Editor: Multi-Resolution Cleaning of Multivariate Time Series Via Detect-Localize-Repair
Li, C., Ma, C., Yu, X., Li, C., & Meng, X. (2026) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65706.2026.00249
“1) Datasets: (1) SWaT [26] records 51 sensors in an industrial control system over 7 days of correct and 4 days of erroneous operations.”
Advancing Decision Tree-Based Online Inference for Edge Computing
Abate, L., & Barbareschi, M. (2026) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c68850.2026.00053
“Our study considers two public datasets for time-series anomaly detection, SWAT [17] and SMD [18], together with one synthetic dataset generated specifically for this work, whose main characteristics are summarized in Table II.”
Sajjadi, P., Dinmohammadi, F., & Shafiee, M. (2026) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad69956.2026.11643181
“A6, collected from the Secure Water Treatment (SWaT) testbed developed by iTrust at the Singapore University of Technology and Design [27].”
Liu, J., & Pi, D. (2026) · Measurement · doi:10.1016/j.measurement.2026.122929
Anomaly Detection in Cyber-Physical Systems Using Recurrent Graphs
Shiryaev, N. A., Novikova, E. S., Zhao, J., et al. (2026) · doi:10.1109/neuront71829.2026.11651404
“The 2015 version of the SWaT (Secure Water Treatment) dataset [15] was selected as the test dataset; this dataset consists of logs collected from a test bench simulating the operation of water treatment facilities.”
Graph-Based Intelligence for Anomaly Detection in Cyber-Physical and IoT Systems
Alzahrani, M. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1017535
Zhang, Q., Xu, Z., Wang, W. W., et al. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3726998
“(cid:2) We conduct extensive experiments on the SWaT [22] and WADI [23] industrial benchmarks, systematically evaluating boundary inflation ratios, detection accuracy, and convergence speed.”
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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