Unsupervised Surrogate Anomaly Detection

Simon Klüttermann

doi:10.1007/978-3-032-05962-8_5

Unsupervised Surrogate Anomaly Detection

2026-01-01

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DOI：10.1007/978-3-032-05962-8_5

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Abstract

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In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e., regular patterns of normal data, which anomalies are deviating from. Inspired by a similar concept in engineering, we refer to our methodology as surrogate anomaly detection. We formalize the concept of surrogate anomaly detection into a set of axioms required for optimal surrogate models and propose a new algorithm, named DEAN (Deep Ensemble ANomaly detection), designed to fulfill these criteria. We evaluate DEAN on 121 benchmark datasets, demonstrating its competitive performance against 19 existing methods, as well as the scalability and reliability of our method.

Keywords:

Anomaly Detection

Ensemble Methods

Unsupervised Surrogate Anomaly Detection

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