Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders
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.
| 2022 | |
|
Mathematics of computing Time series analysis Networks Network monitoring Variational Autoencoders Time-Series Anomaly Detection |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
|
https://dl.acm.org/doi/10.1145/3517745.3563033#sec-terms
https://hdl.handle.net/20.500.12008/34457 |
|
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Sumario: | Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica. |
|---|
