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'''Anomaly detection''' is the process of identifying data points, events, or observations that deviate significantly from the expected pattern in a [[dataset]]. These deviations are referred to as '''anomalies''', '''outliers''', '''novelties''', '''noise''', '''deviations''', or '''exceptions'''.

== Overview ==

In various domains such as [[fraud detection]], [[network security]], [[fault detection]], [[system health monitoring]], and [[event detection systems in sensor networks]], anomaly detection has become a critical component. It provides valuable insights and aids in decision making.

== Types of Anomalies ==

Anomalies can be broadly categorized into three types:

* '''Point anomalies''': A single instance of data is anomalous if it's too far off from the rest.

* '''Contextual anomalies''': The abnormality is context specific. This type of anomaly is common in time-series data.

* '''Collective anomalies''': A set of data instances collectively helps in detecting anomalies.

== Techniques for Anomaly Detection ==

Various techniques have been developed for anomaly detection, some of which include:

* '''Statistical Methods''': These methods model the normal behavior and then determine the likelihood of a particular data point belonging to this model.

* '''Machine Learning Based Methods''': These methods use [[machine learning]] algorithms to predict the next data point. If the prediction deviates significantly from the actual value, it is considered an anomaly.

* '''Distance Based Methods''': These methods calculate the distance between data points. Data points that are significantly far from others are considered anomalies.

* '''Density Based Methods''': These methods identify regions of low density as anomalies.

== Applications of Anomaly Detection ==

Anomaly detection has a wide range of applications including:

* '''Fraud Detection''': In banking and financial sectors, anomaly detection systems can be used to detect unusual transactions, which could be fraudulent.

* '''Intrusion Detection''': In network security, anomaly detection can help in identifying unusual patterns that might indicate a network or system intrusion.

* '''Fault Detection''': In critical systems, anomaly detection can help in identifying faults or failures before they affect the entire system.

== See Also ==

* [[Machine learning]]
* [[Artificial intelligence]]
* [[Data mining]]
* [[Pattern recognition]]
* [[Outlier]]

[[Category:Data analysis]]
[[Category:Machine learning]]
[[Category:Artificial intelligence]]
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Anomaly detection - Revision history

Prab at 18:24, 18 March 2025

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