Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders
- Autor(es):
- García González, Gastón ; Casas, Pedro ; Fernández, Alicia ; Gómez, Gabriel
- Tipo:
- Conferencia
- Versión:
- Publicado
- Resumen:
-
We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). DC-VAE detects anomalies in MTS data through a single model, exploiting temporal and spatial MTS information. We showcase DC-VAE in different MTS datasets, and portray its future application in a continual learning framework, exploiting the generative properties of the underlying generative model to deal with continuously evolving data, avoiding catastrophic forgetting. We showcase the functioning of DC-VAE in the event of concept drifts, and propose the application of a novel approach to generative-driven continual learning, introducing the Deep Generative Replay model.
- Año:
- 2022
- Idioma:
- Inglés
- Temas:
- Mathematics of computing
Time series analysis
Networks
Network monitoring
Variational Autoencoders
Time-Series
Anomaly Detection
- Institución:
- Universidad de la República
- Repositorio:
- COLIBRI
- Enlace(s):
- https://dl.acm.org/doi/10.1145/3517745.3563033#sec-terms
https://hdl.handle.net/20.500.12008/34457
- Nivel de acceso:
- Acceso abierto
