Anomaly Detection with ML
Unsupervised Learning
This is the most common method of anomaly detection. The ML model is trained using an unlabelled dataset. Therefore there is an assumption that the majority of the data in the dataset are normal examples. Any data that differs significantly from the normal behavior will be flagged as an anomaly.
Supervised Learning
Is a less common method since this process requires a large number of positive and negative examples which is difficult since anomalous examples are rare.
