We address the problem of detecting "anomalies" in the network traffic produced by a large population of end-users following a distribution-based change detection approach. In the considered scenario, different traffic variables are monitored at different levels of temporal aggregation (timescales), resulting in a grid of variable/timescale nodes. For every node, a set of per-user traffic counters is maintained and then summarized into histograms for every time bin, obtaining a timeseries of empirical (discrete) distributions for every variable/timescale node. Within this framework, we tackle the problem of designing a formal Distribution-based Change Detector (DCD) able to identify statistically-significant deviations from the past behavior of each individual timeseries. For the detection task we propose a novel methodology based on a Maximum Entropy (ME) modeling approach. Each empirical distribution (sample observation) is mapped to a set of ME model parameters, called "characteristic vector", via closed-form Maximum Likelihood (ML) estimation. This allows to derive a detection rule based on a formal hypothesis test (Generalized Likelihood Ratio Test, GLRT) to measure the coherence of the current observation, i.e., its characteristic vector, to the given reference. The latter is dynamically identified taking into account the typical non-stationarity displayed by real network traffic. Numerical results on synthetic data demonstrates the robustness of our detector, while the evaluation on a labeled dataset from an operational 3G cellular network confirms the capability of the proposed method to identify real traffic anomalies. (C) 2013 Elsevier B.V. All rights reserved.
