Anomaly Detection - Page 3

Anomaly detection identifies observations that differ significantly from an expected pattern. A bank may use it to flag unusual transactions, a manufacturer to spot equipment faults, or a security platform to detect suspicious network activity. Some systems learn from labeled examples of normal and abnormal events, while others model normal behavior and treat large deviations as potential anomalies. Rare does not always mean harmful, so alerts require context and careful thresholds. Effective detection balances missed events against false alarms, adapts as behavior changes, and gives reviewers enough information to investigate why a record, sequence, or sensor reading was considered unusual.