Name
Abstract | ||
|---|---|---|
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 NetFlow data. However, Sampled NetFlow 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 NetFlow 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 (~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 NetFlow data and achieve good accuracy in the presence of sampling. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.comnet.2010.11.002 | Computer Networks |
Keywords | DocType | Volume |
Traffic classification,Machine learning,Network management | Journal | 55 |
Issue | ISSN | Citations |
5 | Computer Networks | 32 |
PageRank | References | Authors |
1.56 | 36 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valentín Carela-Español | 1 | 101 | 6.04 |
| Pere Barlet-ros | 2 | 269 | 27.74 |
| Albert Cabellos-Aparicio | 3 | 418 | 46.33 |
| Josep Solé-pareta | 4 | 436 | 58.67 |