Hierarchical Real-time Network Traffic Classification Based on ECOC - Yaou Zhao; Xiao Xie; Mingyan Jiang (Institute of Advanced Engineering and Science, 2014)
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. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3877