arXiv:2509.09030v1 Announce Type: new Abstract: Anomaly detection is critical in domains such as cybersecurity and finance, especially when working with large-scale tabular data. Yet, unsupervised anomaly detection — where no labeled anomalies are available — remains a significant challenge. Although various deep learning methods have been proposed to model a dataset's joint distribution, real-world tabular data often contain heterogeneous contexts (e.g., different users), making globally rare events normal under certain contexts. Consequently, relying on a single global distribution can overlook these contextual nuances, degrading detection performance. In this paper, we present a context-conditional anomaly detection framework tailored for tabular datasets. Our approach automatically identifies context features and models the conditional data distribution using a simple deep autoencoder. Extensive experiments on multiple tabular benchmark datasets demonstrate that our method outperforms state-of-the-art approaches, underscoring the importance of context in accurately distinguishing anomalous from normal instances.
Original: https://arxiv.org/abs/2509.09030