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

Abstract : 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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Contributor : Alexandre Lung-Yut-Fong Connect in order to contact the contributor
Submitted on : Tuesday, September 20, 2011 - 3:41:50 PM
Last modification on : Tuesday, November 16, 2021 - 11:28:07 AM
Long-term archiving on: : Wednesday, December 21, 2011 - 2:22:27 AM


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Alexandre Lung-Yut-Fong, Céline Lévy-Leduc, Olivier Cappé. Distributed detection/localization of change-points in high-dimensional network traffic data. Statistics and Computing, Springer Verlag (Germany), 2011, pp.1-12. ⟨10.1007/s11222-011-9240-5⟩. ⟨hal-00420862v2⟩



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