Anomaly detection is a cornerstone of machine learning with applications spanning healthcare, fraud detection, and scientific discovery. Despite extensive research, fair bench-marking remains a significant challenge due to the unsupervised nature of anomaly detection. Hyperparameter selection, a crucial determinant of algorithm performance, is often overlooked or bi-ased, leading to inflated or misleading results. Current practices, including reliance on default configurations, random choices, or limited optimization, hinder reproducibility and impede progress. This work presents a novel pipeline for standardized hyperparameter optimization in anomaly detection. Leveraging a curated collection of nearly 500 datasets, the largest of its kind, our approach systematically optimizes over 80 hyperparameters for 13 widely used anomaly detection algorithms. Our comparison re-veals that the performance variance from hyperparameters often surpasses inter-algorithm differences, emphasizing the need for hyperparameter-specific evaluations. We establish a reproducible foundation for anomaly detection research by providing open-access datasets and code. Our findings not only challenge existing evaluation norms but also pave the way for more robust and reliable comparisons toward better anomaly detection research.

Anomaly detection has been a major research area in machine learning with deep ensemble models showing exceptional performance. However, formal verification of robustness for anomaly detection in general, and ensemble models in particular, has been mostly neglected. Moreover, given an already trained, non-robust model, there is no way to adapt it for robustness as a post-processing step as of yet. By harnessing properties of ensemble methods - in particular of the DEAN model - we are the first to post-robustify a model via submodel selection. Beyond this new capability, our method significantly increases verification scalability by employing the inherent properties of ensemble methods. Our experiments show that the DEAN model is most suitable for our method: it proves to be the most robust from the start, allows for post-robustification and keeps a stable runtime across all datasets considered.