计算机科学
交通分类
字节
服务质量
有效载荷(计算)
人工智能
网络数据包
计算机网络
协议(科学)
异常检测
钥匙(锁)
数据挖掘
深包检验
计算机安全
操作系统
病理
医学
替代医学
作者
Guorui Xie,Qing Li,Yong Jiang,Tao Dai,Gengbiao Shen,Rui Li,Richard Sinnott,Shu‐Tao Xia
出处
期刊:ACM Special Interest Group on Data Communication
日期:2020-08-10
被引量:18
标识
DOI:10.1145/3405671.3405811
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI