Abstract

International audience 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. Document type: Part of book or chapter of book