Three broad categories of anomaly detection techniques exist. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.

This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. In data analysis, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Security information and event management (SIEM).

Host-based intrusion detection system (HIDS).

What is anomaly in database