Steps Towards Continual Learning in Multivariate Time-Series Anomaly Detection using Variational Autoencoders

Abstract

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.