Mining frequent closed flows based on approximate support with a sliding window over packet streams

Abstract

Due to the varying and dynamic characteristics of network traffic, the analysis of traffic flows is of paramount importance for network security. In this context, the main challenge consists in mining the traffic flows with high accuracy and limited memory consumption. In this respect, we introduce a novel algorithm, which mines the approximate closed frequent patterns over a stream of packets within a sliding window model. The latter is based on a relaxation rate parameter as well as an approximate support concept. Our experiment results show the robustness and efficiency of our new algorithm against those in the literature.