首页|A Model of Encrypted Network Traffic Classification that Trades Off Accuracy and Efficiency
A Model of Encrypted Network Traffic Classification that Trades Off Accuracy and Efficiency
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
As the Internet industry evolves, the need for effective encrypted traffic classifica-tion (ETC) becomes critical for network management and cybersecurity. Meanwhile, existing deep learning (DL)-based methods struggle to balance model complexity with accuracy. A major challenge is to deploy these models on dominant network devices in a way that ensures fast and accurate traffic classification. In this paper, we propose FasterTrafficNet, a novel DL-based lightweight ETC strategy designed for deployment on low-configuration network devices. We have designed the core com-ponent of FasterTrafficNet using a PConv-based approach that efficiently extracts spatial features from data while reducing unnecessary computation and memory access, further enhancing the operational efficiency of the model. In addition, we integrated Do-Conv in place of conventional non-dot convolution to significantly increase the performance of the model without increasing the computational over-head during inference. We conducted a comparative analysis of FasterTrafficNet against seven advanced ETC methods using four publicly available benchmark datasets. Experimental results show that FasterTrafficNet, with 1.46 million model parameters, provides superior classification performance compared to the other methods. As a result, FasterTrafficNet demonstrates a lightweight approach to ETC that can be applied to large networks of devices.
Deep LearningConvolutional neural networksLightweight modelEncrypted traffic classification
Lancan Yu、Jianting Yuan、Jin Zheng、Nan Yang
展开 >
Xinjiang University, Urumqi 830000, Xinjiang, China
Sichuan Anying Tianhua Technology Co Ltd, Chengdu 610000, Sichuan, China
State Grid Wulumuqi Electric Power Supply Company, Urumqi 83000, Xinjiang, China
NETL
NSTL
Springer Nature
