One-Class SVM

One-Class SVM is used to model normal behavior when labeled anomaly examples are scarce or unavailable. The system learns the region where normal observations concentrate and flags anything outside that boundary as suspicious. It is valuable in domains such as cybersecurity, fault detection, and rare-event analysis. However, performance can be strongly affected by feature scaling, kernel choice, and dataset size.