Name
Abstract | ||
|---|---|---|
In this paper we present an approach for online traffic classification based on the statistical analysis of protocol behaviour at IP level. Then we use the statistics of protocol attribute and Bayesian network method to build a classifier,which can classify unknown flows dynamically as packets pass through the classifier, deciding if a flow belongs to a given application. Distinct from other methods, we use the “universe inter-arrival time” to overcome the influence of RTT variance so that the statistic of universe inter-arrival time is site-independent and time-independent. At last, the experimental results show that our approach performs better than other methods using tradition inter-arrival time. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/SKG.2008.53 | SKG |
Keywords | Field | DocType |
bayesian methods,statistical analysis,bayesian network,traffic classification,protocols,servers,accuracy,transport protocols,internet | Traffic classification,Data mining,Statistic,Computer science,Network packet,Server,Bayesian network,Classifier (linguistics),The Internet,Bayesian probability | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minhuo Hong | 1 | 3 | 0.85 |
| Rentao Gu | 2 | 25 | 8.24 |
| Hongxiang Wang | 3 | 6 | 6.12 |
| Yuefeng Ji | 4 | 303 | 49.02 |