Studying properties of water data using manifold-aware anomaly detectors
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Design of Cyber-Secure Water Plants. Hrsg. / Aditya Mathur; Jianying Zhou; Gauthama Raman. MDPI AG, 2024. (Water).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Studying properties of water data using manifold-aware anomaly detectors
AU - Paulsen, Tino
AU - Brefeld, Ulf
PY - 2024/4/23
Y1 - 2024/4/23
N2 - Deep learning methods, especially the family of autoencoder architectures, exhibit state-of-the-art detection rates in anomaly detection tasks. Additionally, recent results show that learning latent spaces with deep architectures on Riemannian manifolds may further improve performances as well as related interpolation tasks. In this paper, we study the use of Riemannian manifolds with variational autoencoders (VAEs) for anomaly detection on data from water providers. Besides traditional embeddings in Euclidean space, we study embeddings in Poincaré disc, spheres, and Stiefel manifolds, where in general, the Poincaré disc is often preferred for data with hierarchical structures, embeddings in spheres suggests itself for cyclical structures and the Stiefel manifold is well suited for time-dependent data. Data from water providers clearly meets all three criteria and we report on empirical results with the different manifolds as latent spaces and compare their detection performance to that of standard Euclidean embeddings.
AB - Deep learning methods, especially the family of autoencoder architectures, exhibit state-of-the-art detection rates in anomaly detection tasks. Additionally, recent results show that learning latent spaces with deep architectures on Riemannian manifolds may further improve performances as well as related interpolation tasks. In this paper, we study the use of Riemannian manifolds with variational autoencoders (VAEs) for anomaly detection on data from water providers. Besides traditional embeddings in Euclidean space, we study embeddings in Poincaré disc, spheres, and Stiefel manifolds, where in general, the Poincaré disc is often preferred for data with hierarchical structures, embeddings in spheres suggests itself for cyclical structures and the Stiefel manifold is well suited for time-dependent data. Data from water providers clearly meets all three criteria and we report on empirical results with the different manifolds as latent spaces and compare their detection performance to that of standard Euclidean embeddings.
KW - Informatics
UR - https://itrust.sutd.edu.sg/first-international-conference-on-the-design-of-cyber-secure-water-plants-dcs-water24/paper-submission-dcs-water24/
UR - https://www.mdpi.com/journal/water/special_issues/7UA9S2ASB0
M3 - Article in conference proceedings
T3 - Water
BT - Design of Cyber-Secure Water Plants
A2 - Mathur, Aditya
A2 - Zhou, Jianying
A2 - Raman, Gauthama
PB - MDPI AG
T2 - First International Conference on the design of cyber-secure water plants - DCS-Water'24
Y2 - 23 April 2024 through 24 April 2024
ER -