Anomaly detection

term_id: anomaly_detection

Category: basic_concepts

Definition

Anomaly detection, also known as outlier detection, involves analyzing data to find patterns that do not conform to expected behavior. It is widely used in cybersecurity, fraud detection, and system monitoring to identify potential threats or errors. Techniques range from statistical methods to machine learning models like isolation forests and autoencoders, which learn normal behavior and flag deviations as anomalies.

Summary

The process of identifying rare items, events, or observations that deviate significantly from the majority of the data.

Key Concepts

  • Outliers
  • Pattern recognition
  • Fraud detection
  • Statistical deviation

Use Cases

  • Credit card fraud detection
  • Network intrusion detection
  • Industrial fault diagnosis

Code Example

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from sklearn.ensemble import IsolationForest
model = IsolationForest(contamination=0.1)
model.fit(data)

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