Toward supervised anomaly detection
Research output: Journal contributions › Journal articles › Research › peer-review
Authors
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies.
| Original language | English |
|---|---|
| Journal | Journal of Artificial Intelligence Research |
| Volume | 46 |
| Pages (from-to) | 235-262 |
| Number of pages | 28 |
| ISSN | 1076-9757 |
| DOIs | |
| Publication status | Published - 20.02.2013 |
| Externally published | Yes |
- Informatics - learning strategies, Detection accuracy, Empirical studies, Network intrusion detection, Optimization problems, redictive performance, Supervised classifiers, Unsupervised anomaly detection
- Business informatics