Anomaly detection
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[edit | edit source]
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[edit | edit source]
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[edit | edit source]
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[edit | edit source]
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[edit | edit source]
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Contributors: Prab R. Tumpati, MD