Título:
On the quest for foundation generative-AI models for anomaly detection in time-series data.
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Autor:
García González, Gastón
Casas, Pedro
Martínez, Emilio
Fernández, Alicia
Casas, Pedro
Martínez, Emilio
Fernández, Alicia
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Tipo de documento:
Preprint
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Palabras clave:
Multivariate Time-Series Data
Anomaly De- tection
Generative AI
VAE
Foundation Models
Anomaly De- tection
Generative AI
VAE
Foundation Models
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Año de publicación:
2024
Contenido:
Resumen:
Network security data generally consists of hundreds of counters periodically collected in the form of time-series, resulting in a complex-to-analyze multivariate time-series (MTS) process. We investigate a novel approach to time-series modeling, inspired by the successes of large pre-trained foundation models. We introduce FAE (Foundation Auto-Encoders), a foundation generative-AI model for anomaly detection in time-series data, based on Variational Auto-Encoders (VAEs). By foundation, we mean a model pre-trained on massive amounts of time-series data which can learn complex temporal patterns useful for accurate modeling, forecasting, and detection of anomalies on previously unseen datasets. Based on the DC-VAE architecture originally designed for multivariate anomaly detection, FAE leverages VAEs and Dilated Convolutional Neural Networks (DCNNs) to build a generic model for univariate time-series modeling, which could eventually perform properly in out-of-the-box, zero-shot anomaly detection applications. We introduce the main concepts and ideas of this foundation model, and present some preliminary results in a multi-dimensional network monitoring dataset, collected from an operational mobile Internet Service Provider (ISP). This work represents a significant step forward in the development of foundation generative-AI models for anomaly detection in time-series analysis, with applications spanning cybersecurity, network management, and beyond.
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Financiadores:
Este trabajo ha sido parcialmente financiado por Austrian FFG ICT-of-the-Future project DynAISEC – Adaptive AI/ML for Dynamic Cybersecurity Systems – ID887504,y por el proyecto uruguayo CSIC project with reference CSIC-I+D-22520220100371UD Generalization and Domain Adaptation in Time-Series Anomaly Detection.
Citación:
García González, G., Casas, P., Martínez, E. y otros. On the quest for foundation generative-AI models for anomaly detection in time-series data [Preprint]. Publicado en: 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Vienna, Austria, 08-12 jul. 2024, pp. 252-260.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imagenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| GCMF24a.pdf | Preprint | 1.46 MB | Adobe PDF |