A generalizable dynamic flow pairing method for traffic classification
Abstract The goal of network traffic classification is to identify the protocols or types of protocols in the network traffic. In particular, the identification of network traffic with high resource consumption, such as peer-to-peer ( P2P ) traffic, represents a great concern for Internet Service Providers (ISP) and network managers. Most current flow-based classification approaches report high accuracy without paying attention to the generalization ability of the classifier. However, without this ability, a classifier may not be suitable for on-line classification. In this paper, a number of experiments on real traffic help to elucidate the reason for this lack of generalization. It is also shown that one way to attain the generalization ability is by using dynamic classifiers. From these results, a dynamic classification approach based on the pairing of flows according to a similarity criterion is proposed. The pairing method is not a classifier by itself. Rather, its goal is to determine in a fast way that two given flows are similar enough to conclude they correspond to the same protocol. Combining this method with a classifier, most of the flows do not need to be explicitly evaluated by the later, so that the computational overhead is reduced without a significant reduction in accuracy. In this paper, as a case study, we explore complementing the pairing method with payload inspection. In the experiments performed, the pairing approach generalizes well to traffic obtained in different conditions and scenarios than that used for calibration. Moreover, a high portion of the traffic unclassified by payload inspection is categorized with the pairing method.