An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data
This paper proposes a novel framework for multivariate time-series anomaly detection that leverages adversarial learning and contrastive loss within a sequence-based Variational Autoencoder (VAE) architecture, and introduces encoder and decoder adaptor layers that align feature distributions across domains while preser...