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

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Бібліографічні деталі
Опубліковано в::	European Conference on Cyber Warfare and Security (Jun 2025), p. 614-622
Автор:	Rüb, Matthias
Інші автори:	Grüber, Jens, Schotten, Hans
Опубліковано:	Academic Conferences International Limited
Предмети:	Cybersecurity Gait Methods Datasets Anomalies Artificial intelligence Time series Biometrics Synthetic data Case studies Classification Training Health services Property Telecommunications Data quality Health care Feasibility Data
Онлайн доступ:	Citation/Abstract Full Text Full Text - PDF
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Опис
Короткий огляд:	In cybersecurity, synthetic data is beneficial for testing, training, and enhancing Al-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.
Джерело:	Political Science Database