Random Projection and Multiscale Wavelet Leader Based Anomaly Detection and Address Identification in Internet Traffic

Abstract

We present a new anomaly detector for data traffic, `SMS', based on combining random projections (sketches) with multiscale analysis, which has low computational complexity. The sketches allow `normal' traffic to be automatically and robustly extracted, and anomalies detected, without the need for training data. The multiscale analysis extracts statistical descriptors, using wavelet leader tools developed recently for multifractal analysis, without any need for timescales to be selected a priori. The proposed detector is illustrated using a large recent dataset of Internet backbone traffic from the MAWI archive, and compared against existing detectors.