SMILER: Towards Practical Online Traffic Classification

Abstract

Network traffic classification is extremely important in numerous network functions today. However, most of the current approaches based on port number or payload detection are becoming increasingly impractical with the appearance of dynamic or encrypted applications. Even though some supervised learning based work were proposed, it is difficult to collect sufficient flow-labeled traces for training. On the other hand, online classification needs an early identification, which is still challenging for most well-known approaches. In this paper, we propose a semi-supervised learning based traffic classification approach named SMILER, which supports an early classification from the sizes of the first few packets (empirically 5 packets) of a flow. Experiments in real networks demonstrate that SMILER achieves 94% precision and 96% recall on average for all tested applications, even with disordered packets SMILER still works well. With a hybrid scheme, the performance is further improved. Meanwhile, SMILER performs fast in both classification and updating. All experimental results show that SMILER is practical for fast and accurate online traffic classification.