Anomaly Detection Software deals with the identification of events, observations or items that do not follow a specified pattern. Some anomaly detection techniques that are not supervised detect anomalies in a data set that is unlabeled by assuming that most of the instances in the dataset are quite reasonable. A labeled test data set is used for supervised detection techniques. The data set is labeled as abnormal and normal, and a classifier is trained. Anomaly detection software allows companies to detect anomalies by spotting uncommon traffic, unexpected behaviors and unusual patterns. The software compares patterns, events, and items to measure any deviation from the baseline. The software can also be used to detect anomalies in the unlabeled data set, and it locates unusual activities by using a model representing normal behaviors and patterns. The software uses techniques like statistical methods, cluster-based techniques, and density-based techniques. Business users will also find it useful as it helps them identify collective anomalies, single anomalies as well as contextual anomalies. They can locate behaviors that show intrusion, detect fraudulent acts and monitor their business systems.