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Détection de ruptures pour les signaux multidimensionnels. Application à la détection d'anomalies dans les réseaux.

Abstract : The aim of this work is to propose non-parametric change-point detection methods. The main application of such methods is the use of data recorded by a collection of network sensors to detect malevolent attacks. The first contribution of the thesis work is a decentralized anomaly detector. Each network sensor applies a rank-based change-point detection test, and the final decision is taken by a fusion center which aggregates the information transmitted by the sensors. This method is able to process a huge amount of data, thanks to a clever filtering step. In the second contribution, we take into account the dependencies between the different sensors to improve the detection performance. Based on homogeneity tests that we have proposed to assess the similarity between different sets of data, the robust detection methods that we have designed are able to find one or more change-point in a multidimensional signal. We thus obtained robust and versatile methods, with strong theoretical properties, to solve a large collection of segmentation problems: network anomaly detection, econometrics, DNA analysis for cancer prognosis... The methods that we proposed are particularly adequate when the characteristics of the analyzed data are unknown.
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https://pastel.archives-ouvertes.fr/pastel-00675543
Contributor : Alexandre Lung-Yut-Fong <>
Submitted on : Thursday, March 1, 2012 - 1:24:13 PM
Last modification on : Friday, July 31, 2020 - 10:44:07 AM
Long-term archiving on: : Wednesday, December 14, 2016 - 9:10:17 AM

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Alexandre Lung-Yut-Fong. Détection de ruptures pour les signaux multidimensionnels. Application à la détection d'anomalies dans les réseaux.. Méthodologie [stat.ME]. Télécom ParisTech, 2011. Français. ⟨pastel-00675543⟩

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