Dynamic Feature Grouping in Anomaly Detection
Anomaly detection is important in many different areas, among them web security. When performing anomaly detection in web security there can be hundreds of features in the collected web traffic data to consider, even though all of them might not be necessary. This thesis aims to develop a strategy to dynamically create significant parameter groups from web traffic data containing over 100 features