The Isolation Forest model is an unsupervised machine learning algorithm used for anomaly detection in structured data. It is based on the concept of isolating anomalies by constructing random decision trees. The model assumes that anomalies are less frequent and more isolated than normal data points. By randomly selecting features and splitting data along the range of their values, the Isolation Forest isolates anomalies more efficiently and quickly than traditional methods.
The Isolation Forest model is particularly useful in the following scenarios:
Isolation Forest can detect fraudulent activities by identifying anomalies in financial transactions, credit card usage, or insurance claims. By isolating unusual patterns, it helps organizations protect against fraudulent behavior.
The model can be applied to network traffic analysis to identify anomalous network connections or malicious activities that deviate from normal behavior. It aids in the early detection of cyber threats and helps enhance network security.
By analyzing structured data from sensors and machines in manufacturing processes, Isolation Forest helps detect anomalies that indicate equipment malfunction, product defects, or deviations from quality standards.