基于深度报文检测和深度流检测的骨干网流量特征分析

Based on the massive data collected with a passive network monitoring equipment placed in China's backbone, we present a deep insight into the network backbone traffic and evaluate various ways for improving traffic classifying efficiency in this paper. In particular, the study has scrutinized the network traffic in terms of protocol types and signatures, flow length, and port distribution, from which meaningful and interesting insights on the current Internet of China from the perspective of both the packet and flow levels are derived. We show that the classification efficiency can be greatly improved by using the information of preferred ports of the network applications. Quantitatively, we find two traffic duration thresholds, with which 40% of TCP flows and 70% of UDP flows can be excluded from classification processing while the impact on classification accuracy is trivial, i.e., the classification accuracy can still reach a high level by saving 85% of the resources.