HE Hongye, YANG Zhiguo, CHEN Xiangning
(ZTE Corporation, Shenzhen 518057, China)
Traffic identification becomes more important, yet more challenging as related encryption techniques are rapidly developing nowadays. Unlike recent deep learning methods that apply image processing to solve such encrypted traffic problems, in this paper, we propose a method named Payload Encoding Representation from Transformer (PERT) to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique. By implementing traffic classification experiments on a public encrypted traffic data set and our captured Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness than other compared baselines. To the best of our knowledge, this is the first time the encrypted traffic classification with the dynamic word embedding has been addressed.
traffic identification; encrypted traffic classification; natural language processing; deep learning; dynamic word embedding