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

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

2009-09-30

arxiv.org/abs/0909.5524v2

Statistics

Applications

Statistics and Computing (2011) 1-12

Scientific paper

10.1007/s11222-011-9240-5

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

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