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In data analysis, anomaly detection (also called outlier detection)[1] is the identification of rare elements, events, or observations that arouse suspicion because of their significant deviation from the bulk of the data.[1] Typically, the anomalous elements lead to a problem such as bank fraud, a structural defect, medical problems, or errors in a text. Anomalies are also called outliers, novelties, noise, deviations, and exceptions.[2]
Particularly in the context of abuse and network attack detection, the objects of interest are often not rare objects, but unexpected spikes in activity. This pattern does not fit the usual statistical definition of an outlier as a rare object, and many outlier detection methods (especially unsupervised methods) fail on such data unless they have been appropriately aggregated. Instead, a cluster analysis algorithm may be able to detect the microclusters formed by these patterns.[3]
There are three broad categories of anomaly detection techniques.[4] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test dataset, assuming that the majority of instances in the dataset are normal, by looking for instances that appear to fit the rest of the dataset the least. Supervised anomaly detection techniques require a dataset that has been labeled as "normal" and "abnormal" and involve training a classifier (the key difference from many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training dataset, and then test the probability that a test instance will be generated by the model used.
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