Feasibility of Conditional Variational Autoencoders for Phase-Averaged Synthetic Time Series
Guardado en:
| Publicado en: | European Conference on Cyber Warfare and Security (Jun 2025), p. 614-622 |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Academic Conferences International Limited
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3244089444 | ||
| 003 | UK-CbPIL | ||
| 035 | |a 3244089444 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 142231 |2 nlm | ||
| 100 | 1 | |a Rüb, Matthias |u Intelligent Networks Research Group, German Research Center for Artificial Intelligence, Kaiserslautern, Germany | |
| 245 | 1 | |a Feasibility of Conditional Variational Autoencoders for Phase-Averaged Synthetic Time Series | |
| 260 | |b Academic Conferences International Limited |c Jun 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a 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. | |
| 653 | |a Cybersecurity | ||
| 653 | |a Gait | ||
| 653 | |a Methods | ||
| 653 | |a Datasets | ||
| 653 | |a Anomalies | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Time series | ||
| 653 | |a Biometrics | ||
| 653 | |a Synthetic data | ||
| 653 | |a Case studies | ||
| 653 | |a Classification | ||
| 653 | |a Training | ||
| 653 | |a Health services | ||
| 653 | |a Property | ||
| 653 | |a Telecommunications | ||
| 653 | |a Data quality | ||
| 653 | |a Health care | ||
| 653 | |a Feasibility | ||
| 653 | |a Data | ||
| 700 | 1 | |a Grüber, Jens |u Intelligent Networks Research Group, German Research Center for Artificial Intelligence, Kaiserslautern, Germany | |
| 700 | 1 | |a Schotten, Hans |u Intelligent Networks Research Group, German Research Center for Artificial Intelligence, Kaiserslautern, Germany | |
| 773 | 0 | |t European Conference on Cyber Warfare and Security |g (Jun 2025), p. 614-622 | |
| 786 | 0 | |d ProQuest |t Political Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244089444/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3244089444/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244089444/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |