Deep Context-Conditioned Anomaly Detection for Tabular Data

• URL: http://arxiv.org/abs/2509.09030v1
• Date: Wed, 10 Sep 2025 22:01:11 GMT
• Title: Deep Context-Conditioned Anomaly Detection for Tabular Data
• Authors: Spencer King, Zhilu Zhang, Ruofan Yu, Baris Coskun, Wei Ding, Qian Cui,
• Abstract summary: Anomaly detection is critical in domains such as cybersecurity and finance.<n>In this paper, we present a context-conditional anomaly detection framework.<n>Our approach automatically identifies context features and models the conditional data distribution.
• Score: 9.58464841713335
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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