Publication
Feasibility of Conditional Variational Autoencoders for Phase-Averaged Synthetic Time Series
Matthias Rüb; Jens Grüber; Hans Dieter Schotten
In: Proceedings of the European Conference on Cyber Warfare and Security. European Conference on Cyber Warfare and Security (ECCWS-2025), June 26-27, Germany, Vol. 24, No. 1, ISBN 978-1-917204-45-3, aci, 2025.
Abstract
In cybersecurity, synthetic data is beneficial for testing, training, and enhancing AI-driven defense systems without compromising sensitive information. Critical sectors like telecommunications, finance, energy, and healthcare generate vast amounts of time-series data, often requiring reduction methods such as phase-averaging to manage scale. However, this can obscure essential features, impacting anomaly detection and threat modeling. This study explores whether conditional Variational Autoencoders (cVAEs) can generate high-quality synthetic data when given only phase-averaged time series for training. Results on a biometric use-case show that cVAEs preserve intrinsic properties of reduced data, making it usable for classification and to a more restricted degree as training data in downstream cybersecurity applications.
Projects
More links
https://papers.academic-conferences.org/index.php/eccws/article/view/3480
,
https://papers.academic-conferences.org/index.php/eccws/article/view/3480/3325 ,
https://www.researchgate.net/publication/393048373_Feasibility_of_Conditional_Variational_Autoencoders_for_Phase-Averaged_Synthetic_Time_Series
https://papers.academic-conferences.org/index.php/eccws/article/view/3480/3325 ,
https://www.researchgate.net/publication/393048373_Feasibility_of_Conditional_Variational_Autoencoders_for_Phase-Averaged_Synthetic_Time_Series