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We propose a novel approach for distributed statistical detection ofchange-points in high-volume network traffic. We consider more specifically thetask of detecting and identifying the targets of Distributed Denial of Service(DDoS) attacks. The proposed algorithm, called DTopRank, performs distributednetwork anomaly detection by aggregating the partial information gathered in aset of network monitors. In order to address massive data while limiting thecommunication overhead within the network, the approach combines recordfiltering at the monitor level and a nonparametric rank test for doublycensored time series at the central decision site. The performance of theDTopRank algorithm is illustrated both on synthetic data as well as from atraffic trace provided by a major Internet service provider.

Distributed detection/localization of change-points in high-dimensional network traffic data