Name
Abstract | ||
|---|---|---|
P2P traffic identification is an important issue of internet traffic analysis, and machine learning is a viable approach to address it. However, compared to ensemble learning methods, traditional methods and simple machine learning methods appear to be slightly limited in improving performance. In this paper, Random Forests and feature weighted Naive Bayes was integrated to P2P traffic identification. Scores were calculated for each category in the model while the process of prediction. Then, weighted majority voting was used to get the final output. Experiments were conducted to verify the effectiveness and stability of the integrated model, which implements in the programming mode of MapReduce. Results have shown that the model achieved a better overall performance and may provides an alternative way to solve P2P traffic identification problem. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FSKD.2014.6980874 | FSKD |
Keywords | Field | DocType |
p2p traffic identification,parallel processing,mapreduce,ensemble learning model,internet traffic analysis,ensemble learning,weighted majority voting,machine learning methods,bayes methods,learning (artificial intelligence),random forests,internet,ensemble learning methods,feature weighted naive bayes,programming mode,telecommunication traffic,peer-to-peer computing | Traffic identification,Semi-supervised learning,Pattern recognition,Computer science,Artificial intelligence,Ensemble learning,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengxiong Deng | 1 | 1 | 0.68 |
| Jiangtao Luo | 2 | 6 | 3.50 |
| Yong Liu | 3 | 1 | 0.34 |
| Xiaoping Wang | 4 | 1 | 0.34 |
| Junchao Yang | 5 | 1 | 0.34 |