Feasibility of Conditional Variational Autoencoders for Phase-Averaged Synthetic Time Series

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.