Active Anomaly Detection via Ensembles

Understand

In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives.

• One important way to configure the anomaly detector is by providing true labels for a few instances.
• We study the problem of label-efficient active learning to automatically tune anomaly detection ensembles and make four main contributions.
• First, we present an important insight into how anomaly detector ensembles are naturally suited for active learning.