What are the differences between supervised and unsupervised anomaly detection?

What are the differences between supervised and unsupervised anomaly detection?

Supervised anomaly detection uses labeled data to train models, while unsupervised does not require labels and identifies anomalies based on patterns.

Can you give examples of each type?

Sure! An example of supervised anomaly detection is fraud detection in banking, where labeled transactions are used. Unsupervised could be network intrusion detection, identifying unusual patterns without prior labels.

Which method is more effective?

It depends on the context. Supervised methods can be more accurate with quality labeled data, while unsupervised methods are useful when labels are unavailable.

What are the challenges of each approach?

Supervised methods require extensive labeled data, which can be costly. Unsupervised methods may produce false positives due to noise in the data.