Name
Abstract | ||
|---|---|---|
Applications are becoming more complicated and diverse as the network environment grows day by day. So, it is important to classify application traffic accurately. Although there are many ways to classify applications traffic, machine learning based approaches are becoming more efficient in nowadays. This is because machine learning methods are more appropriate than existing methods for accurate and efficient applications traffic classification. Payload signature methods have limitations to deal with various patterns and increasing application traffic complexity. In this paper, we propose a method for extracting flow features and a system for classifying applications traffic based on Machine Learning |
Year | Venue | Keywords |
|---|---|---|
2017 | Asia-Pacific Network Operations and Management Symposium-APNOMS | Traffic Classification,Machine Learning,Learning Feature,Flow Feature Extraction,Normalization |
Field | DocType | ISSN |
Traffic classification,Online machine learning,Normalization (statistics),Active learning (machine learning),Computer science,Artificial intelligence,Computational learning theory,Machine learning,Feature learning,Payload | Conference | 2576-8565 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jee-Tae Park | 1 | 0 | 2.70 |
| Kyu-Seok Shim | 2 | 7 | 7.72 |
| Sung-Ho Lee | 3 | 0 | 2.37 |
| Myung-Sup Kim | 4 | 325 | 45.01 |