Mining Approximate Frequent Closed Flows over Packet Streams

Imen Brahmi 1 Sadok Ben Yahia 1 Pascal Poncelet 2
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Due to the varying and dynamic characteristics of network traffic, the analysis of traffic flows is of paramount importance for net- work security, accounting and traffic engineering. The problem of ex- tracting knowledge from the traffic flows is known as the heavy-hitter issue. In this context, the main challenge consists in mining the traffic flows with high accuracy and limited memory consumption. In the aim of improving the accuracy of heavy-hitters identification while having a reasonable memory usage, we introduce a novel algorithm called ACL- Stream. The latter mines the approximate closed frequent patterns over a stream of packets. Carried out experiments showed that our proposed algorithm presents better performances compared to those of the pioneer known algorithms for heavy-hitters extraction over real network traffic traces.
Type de document :
Communication dans un congrès
DaWaK: Data Warehousing and Knowledge Discovery, 2011, Toulouse, France. DaWaK'2011: 13th International Conference on Data Warehousing and Knowledge Discovery, pp.419-431, 2011
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00798308
Contributeur : Pascal Poncelet <>
Soumis le : vendredi 8 mars 2013 - 12:19:21
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00798308, version 1

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Imen Brahmi, Sadok Ben Yahia, Pascal Poncelet. Mining Approximate Frequent Closed Flows over Packet Streams. DaWaK: Data Warehousing and Knowledge Discovery, 2011, Toulouse, France. DaWaK'2011: 13th International Conference on Data Warehousing and Knowledge Discovery, pp.419-431, 2011. 〈lirmm-00798308〉

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