Name
Abstract | ||
|---|---|---|
Internet traffic classification is an essential task for managing large networks. Network design, routing optimization, quality of service management, anomaly and intrusion detection tasks can be improved with a good knowledge of the traffic. Traditional classification methods based on transport port analysis have become inappropriate for modern applications. Payload based analysis using pattern searching have privacy concerns and are usually slow and expensive in computational cost. In recent years, traffic classification based on the statistical properties of flows has become a relevant topic. In this work we analyze the size of the firsts packets on both directions of a flow as a relevant statistical fingerprint. This fingerprint is enough for accurate traffic classification and so can be useful for early traffic identification in real time. This work proposes the use of a supervised machine learning clustering method for traffic classification based on Support Vector Machines. We compare our method accuracy with a more classical centroid based approach, obtaining promising results. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1636682.1636693 | LANC |
Keywords | Field | DocType |
statistical property,support vector machine,relevant statistical fingerprint,traffic classification,accurate traffic classification,early traffic identification,internet traffic classification,clustering method,traditional classification method,method accuracy,relevant topic,early traffic classification,internet traffic,pattern search,network design,intrusion detection,real time,support vector machines | Traffic classification,Data mining,Traffic generation model,Network planning and design,Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Cluster analysis,Linear classifier,Intrusion detection system,Machine learning | Conference |
Citations | PageRank | References |
14 | 0.65 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Gómez Sena | 1 | 21 | 2.45 |
| Pablo Belzarena | 2 | 50 | 10.73 |