A Dataset to Support Research in the Design of Secure Water Treatment Systems

By Jonathan Goh, Sridhar Adepu, K. N. Junejo, Aditya P. Mathur. Critical Information Infrastructures Security, 2016

710 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/26f058a2aedf8bd24ad5cb97170173b5124dbbf8

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Citations in the corpus

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018, methodology): “5The raw data are not plotted in this paper due to page limit—please refer to [37] for more information.”
Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization (2021, methodology): “We use three publicly available datasets, including a server machine dataset published as part of the contributions of this study and two datasets popular in related research [2, 9, 28].”
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (2021, methodology): “Based on author descriptions of anomalies for SWaT [37], WADI [38], DMDS [39], MSL [5] and SMAP [5] datasets, and our own visualizations (e.”
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks (2019, methodology): “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 small”
Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks (2018, methodology): “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).”
USAD: UnSupervised Anomaly Detection on Multivariate Time Series (2020, background): “The SWaT dataset 1 is a scaled down version of a real-world industrial water treatment plant producing filtered water [4].”
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (2021, background): “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.”