Name
Abstract | ||
|---|---|---|
Network flow classification plays a very important role in various network applications and is a fundamental task in network flow control. However, the innovations in the multi-source network application and the elastic network architecture with the network flows of high volume, velocity, variety, and veracity pose unprecedented challenges on accurate network flow classification. In this article, ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MNET.2018.1800078 | IEEE Network |
Keywords | Field | DocType |
Bayes methods,Computational modeling,Network architecture,Unsupervised learning,Hidden Markov models,Telecommunication traffic,Networked traffic control,Task analysis,Classification,Learning systems | Flow network,Data mining,Ethernet flow control,Autoencoder,Traffic flow,Computer science,Network architecture,Supervised learning,Unsupervised learning,Hidden Markov model,Distributed computing | Journal |
Volume | Issue | ISSN |
32 | 6 | 0890-8044 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Li | 1 | 214 | 28.84 |
| Zhikui Chen | 2 | 692 | 66.76 |
| Laurence T. Yang | 3 | 6870 | 682.61 |
| Jing Gao | 4 | 21 | 6.58 |
| Qingchen Zhang | 5 | 372 | 19.17 |
| M Jamal Deen | 6 | 524 | 76.75 |