Unsupervised Surrogate Anomaly Detection
Abstract
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.
Cite
Text
Klüttermann et al. "Unsupervised Surrogate Anomaly Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-05962-8_5Markdown
[Klüttermann et al. "Unsupervised Surrogate Anomaly Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/kluttermann2025ecmlpkdd-unsupervised/) doi:10.1007/978-3-032-05962-8_5BibTeX
@inproceedings{kluttermann2025ecmlpkdd-unsupervised,
title = {{Unsupervised Surrogate Anomaly Detection}},
author = {Klüttermann, Simon and Katzke, Tim and Müller, Emmanuel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2025},
pages = {71-88},
doi = {10.1007/978-3-032-05962-8_5},
url = {https://mlanthology.org/ecmlpkdd/2025/kluttermann2025ecmlpkdd-unsupervised/}
}