Anomaly detection is often approached as a supervised machine learning problem. A typical case is credit card fraud detection. These problems are then reduced to classification problems with imbalanced data. Some of the models used include Decision Tree, Logistic Regression, Random Forest, Ada Boost, XG Boost, Support Vector Machine (SVM), and Light GBM (see:
Essentially, this means that when searching for anomalies, the main thing is to decide what exactly is considered an anomaly.
In a way, how isolated the points should be?