Anomaly Detection

What Anomaly Detection is

Anomalies are rare by definition, so labelled examples are scarce and the problem is often framed as modelling normality and flagging what does not fit.

How it works

Approaches include statistical thresholds, isolation forests, autoencoder reconstruction error, and forecasting residuals on time series. Alert thresholds are tuned against operator tolerance for false positives.

Why it matters

It is the backbone of fraud prevention, security monitoring and infrastructure observability, where the interesting events are the rare ones.

Common uses

• →Payment fraud detection
• →Network intrusion monitoring
• →Manufacturing defect detection
• →Server metric alerting