Toward supervised anomaly detection

  • Authors:

  • Nico Görnitz;Marius Kloft;Konrad Rieck;Ulf Brefeld

  • Affiliations:

  • Machine Learning Laboratory, Technische Universität Berlin, Berlin, Germany and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York;Machine Learning Laboratory, Technische Universität Berlin, Berlin, Germany and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York;University of Göttingen, Dep. of Computer Science, Göttingen, Germany;Technische Universität Darmstadt and German Institute for International Educational Research, Darmstadt, Germany

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.