Statistical Inference and Î±-Stable Modeling for Anomaly Detection in Network Traffic
Anomaly detection aims at finding the presence of anomalous patterns in network traffic. Automatic detection of such patterns can provide network administrators with an additional source of information to diagnose network behavior or finding the root cause of network faults. However, as of today, a commonly accepted procedure to decide whether a given traffic trace includes anomalous patterns is not available. Indeed, several approaches to this problem have been reported in the literature. Research proposals in anomaly detection typically follow a four-stage approach, in which the first three stages define the detection method, while the last stage is dedicated to validate the approach.