| Título: | Hierarchical Real-time Network Traffic Classification Based on ECOC |
| Autores: |
Zhao, Yaou; Shandong University Xie, Xiao; University of Jinan, Jinan Jiang, Mingyan; Shandong University |
| Fecha: | 2013-09-04 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: |
Computer Networks; Computational Intelligence Hierarchical Real-time Model; Network Traffic Classification; ECOC |
| Descripción: | Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P) application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classification methods were diminished.An alternative method based on statistics and machine learning hadattracted researchers' attention in recent years. However, most ofthe proposed algorithms were off-line and usually used a single classifier.In this paper a new hierarchical real-time model was proposed which comprised of a three tuple (source ip, destination ip and destination port)look up table(TT-LUT) part and layered milestone part. TT-LUT was used to quickly classify short flows whichneed not to pass the layered milestone part, and milestones in layered milestone partcould classify the other flows in real-time with the real-time feature selection and statistics.Every milestone was a ECOC(Error-Correcting Output Codes) based model which was usedto improve classification performance. Experiments showed that the proposedmodel can improve the efficiency of real-time to 80%, and themulti-class classification accuracy encouragingly to 91.4% on the datasets which had been captured from the backbone router in our campus through a week. |
| Idioma: | Inglés |