Analysis of the impact of sampling on Net Flow traffic classification

The traffic classification problem has recently attracted the interest of both network operators and researchers. Several machine learning (ML) methods have been proposed in the literature as a promising solution to this problem. Surprisingly, very few works have studied the traffic classification problem with Sampled Net Flow data. However, Sampled Net-Flow is a widely extended monitoring solution among network operators. In this paper we aim to fulfill this gap. First, we analyze the performance of current ML methods with Net-Flow by adapting a popular ML-based technique. The results show that, although the adapted method is able to obtain similar accuracy than previous packet-based methods (approximate to 90%), its accuracy degrades drastically in the presence of sampling. In order to reduce this impact, we propose a solution to network operators that is able to operate with Sampled Net Flow data and achieve good accuracy in the presence of sampling. (C) 2010 Elsevier B.V. All rights reserved.