  We propose a robust variational autoencoder with $\beta$ divergence for
tabular data (RTVAE) with mixed categorical and continuous features.
Variational autoencoders (VAE) and their variations are popular frameworks for
anomaly detection problems. The primary assumption is that we can learn
representations for normal patterns via VAEs and any deviation from that can
indicate anomalies. However, the training data itself can contain outliers. The
source of outliers in training data include the data collection process itself
(random noise) or a malicious attacker (data poisoning) who may target to
degrade the performance of the machine learning model. In either case, these
outliers can disproportionately affect the training process of VAEs and may
lead to wrong conclusions about what the normal behavior is. In this work, we
derive a novel form of a variational autoencoder for tabular data sets with
categorical and continuous features that is robust to outliers in training
data. Our results on the anomaly detection application for network traffic
datasets demonstrate the effectiveness of our approach.
