SAM: Self-attention based deep learning method for online traffic classification

Xie，Guorui1; Li，Qing2; Jiang，Yong1; Dai，Tao1; Shen，Gengbiao1; Li，Rui1; Sinnott，Richard3; Xia，Shutao1

10.1145/3405671.3405811

2020-08-14

NetAI 2020 - Proceedings of the 2020 Workshop on Network Meets AI and ML

14-20

Network traffic classification categorizes traffic classes based on protocols (e.g., HTTP or DNS) or applications (e.g., Facebook or Gmail). Its accuracy is a key foundation of some network management tasks like Quality-of-Service (QoS) control, anomaly detection, etc. To further improve the accuracy of traffic classification, recent researches have introduced deep learning based methods. However, most of them utilize the privacy-concerned payload (user data). Besides, they generally do not consider the dependency of bytes in a packet, which we believe can be exploited for the more accurate classification. In this work, we treat the initial bytes of a network packet as a language and propose a novel Self-Attention based Method (SAM) for traffic classification. The average F1-scores of SAM on protocol and application classification are 98.62% and 98.93%. With the higher accuracy of SAM, better QoS and anomaly detection can be met.

通讯

英语

EI

20204109313677

Anomaly detection ; E-learning ; Learning systems ; Quality of service

2-s2.0-85091998615

Scopus

1.Tsinghua University,Peng Cheng Laboratory,China
2.Southern University of Science and Technology,Peng Cheng Laboratory,China
3.University of Melbourne,Australia

Xie，Guorui,Li，Qing,Jiang，Yong,et al. SAM: Self-attention based deep learning method for online traffic classification[C],2020:14-20.