Wavelet Methods for the Detection of Anomalies and their Application to Network Traffic Analysis

摘要

Here we develop an integrated tool for the online detection of network anomalies. We consider statistical change point detection algorithms, for both local changes in the variance and for the detection of jumps, and propose modified versions of these algorithms based on moving window techniques. We investigate performances on simulated data and on network traffic data with several superimposed attacks. All detection methods are based on wavelet packet transforms.