We show that anomaly detection can be interpreted as a binary classification problem. Using this interpretation we propose a support vector machine (SVM) for anomaly detection. We then present some theoretical results which include consistency and learning rates. Finally, we experimentally compare our SVM with the standard one-class SVM.
I. Steinwart, D. Hush, and C. Scovel, Density Level Detection is Classification. In Neural Information Processing Systems 17, pp. 1337-1344, (2005). Los Alamos National Laboratory Technical Report LA-UR-04-3768. [ Abstract | PDF (104 KB) ]
