Anomaly detection

From WikiMD's Wellness Encyclopedia

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]

Contributors: Prab R. Tumpati, MD