WADI: a water distribution testbed for research in the design of secure cyber physical systems

By Chuadhry Mujeeb Ahmed, Venkata Reddy Palleti, Aditya P. Mathur. CySWATER@CPSWeek, 2017

567 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/4733bd02af7507093a4012eb8bc50698191532f5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Jules Samaran
2. Jonathan Goh
3. Xiuzhen Cheng
4. Astha Garg
5. See-Kiong Ng
6. Zixuan Yuan
7. Jihun Yi
8. Bryan Hooi

In Electrical and Electronic Engineering

1. Xiuzhen Cheng
2. Bryan Hooi
3. Ailin Deng
4. Sungroh Yoon

In Hardware and Architecture

1. Jonathan Goh
2. Xiuzhen Cheng
3. See-Kiong Ng
4. Zixuan Yuan
5. Jihun Yi
6. Bryan Hooi
7. Ailin Deng
8. Dingshuo Chen

Citations in the corpus

• Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, methodology): “In this regard, several real-world datasets (e.g., SWaT and WADI) have been released [57], [58] so that researchers can use them without the need to collect vast numbers of data personally.”
• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (2021, background): “As an extension of SWaT , Water Distribution ( WADI ) is a distribution system comprising a larger number of water distribution pipelines (Ahmed, Palleti, and Mathur 2017).”
• Deep Learning for Time Series Anomaly Detection: A Survey (2022, unknown): “…of HitRate@K, provides a precise measure of inter-pretative performance by quantifying the model’s ability to pinpoint the most relevant factors contributing to each Manuscript submitted to ACM Use when it is crucial to minimise false alarms and ensure that detected anomalies are truly significant.”
• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks (2019, methodology): “The WADI testbed is similarly equipped with chemical dosing systems, booster pumps and valves, instrumentation and analysers [27].”
• Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT (2021, background): “WADI [9] Water Distribution dataset is collected from a water distribution testbed as an extension of the SWaT testbed.”
• An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (2021, methodology): “1 1 100 10 WADI [38] Water distribution 1 123 1209601 172801 5.”