 Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection).Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given "normal" training data set, and then test the likelihood of a test instance to be generated by the learnt model.Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting ecosystem disturbances.