A novel framework for incorporating labeled examples into anomaly detection

This paper presents a principled approach for incorporating labeled examples into an anomaly detection task. We demonstrate that, with the addition of labeled examples, the anomaly detection algorithm can be guided to develop better models of the normal and abnormal behavior of the data, thus improving the detection rate and reducing the false alarm rate of the algorithm. A framework based on the finite mixture model is introduced to model the data as well as the constraints imposed by the labeled examples. Empirical studies conducted on real data sets show that significant improvements in detection rate and false alarm rate are achieved using our proposed framework.