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
Overview
Integrity signals
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Claims the paper makes
Citations
Cites
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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Cited by
Sorted by citations
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Deng, A., & Hooi, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16523 · Cited by 1,413
“Since the systems took 5-6 hours to reach stabilization when first turned on (Goh et al. 2016), we eliminate the first 2160 samples for both datasets.”
BackgroundMAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S.-K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30490-4_56 · Cited by 1,323
“ys. For both of these datasets, we eliminated the rst 21;600 samples from the training data (normal data) since it took 5-6 hours to reach stabilization when the system was rst turned on according to [26]. In the anomaly detection process, we subdivide the original long multiple sequences into smaller time series by t…”
BackgroundMethodInfluentialSelf-citationUSAD: UnSupervised Anomaly Detection on Multivariate Time Series
Audibert, J., Michiardi, P., Guyard, F., Marti, S., & Zuluaga, M. A. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403392 · Cited by 1,094
“The SWaT dataset 1 is a scaled down version of a real-world industrial water treatment plant producing filtered water [4].”
BackgroundDetecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks
Kravchik, M., & Shabtai, A. (2018) · Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC) · doi:10.1145/3264888.3264896 · Cited by 414
“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).”
BackgroundMethodInfluentialAnomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Inoue, J., Yamagata, Y., Chen, Y., POSKITT, C. M., & Sun, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.149 · Cited by 372
“They use stacked LSTMs to detect anomalies, and the same SWaT dataset in their evaluation [12].”
MethodInfluential
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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