Name
Abstract | ||
|---|---|---|
A wide range of traffic classification approaches has been proposed in the last few years by the scientific community. However, the development of complete classification architectures that work directly in real-time on high capacity links is limited. In this paper we present the implementation of a machine-learning technique (SVM), one of the most accurate but most computationally expensive mechanisms, on the CoMo project infrastructure. We show the computational time required to process different traffic traces and the optimization steps we adopted to improve the performance of the system and achieve real-time classification on high-speed links. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IWCMC.2011.5982804 | Wireless Communications and Mobile Computing Conference |
Keywords | Field | DocType |
learning (artificial intelligence),pattern classification,support vector machines,CoMo project infrastructure,machine-learning technique,on-line SVM traffic classification,support vector machine algorithm,Computational time,Machine-learning,On-line traffic classification | Structured support vector machine,Traffic classification,Computer science,Support vector machine,Artificial intelligence,Computational learning theory,Relevance vector machine,Linear classifier,Machine learning,Multiclass classification | Conference |
ISSN | ISBN | Citations |
2376-6492 | 978-1-4244-9539-9 | 12 |
PageRank | References | Authors |
0.67 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alice Este | 1 | 196 | 7.42 |
| Francesco Gringoli | 2 | 890 | 61.65 |
| Luca Salgarelli | 3 | 937 | 81.17 |