FastTraffic: A lightweight method for encrypted traffic fast classification

Abstract

Nowadays, most Internet communications have adopted encrypted network access technology for privacy protection, so encrypted traffic classification (ETC) has become a crucial research point to support network management and ensure cyberspace security. Meanwhile, off-the-shelf deep learning (DL)-based approaches suffer from long preprocessing time, large input size, and a trade-off between model complexity and accuracy. There is a tough challenge to deploy them on mainstream network devices and achieve fast and accurate traffic classification. In this paper, we design FastTraffic, a lightweight DL-based method for ETC on low-configuration network devices. To speed up processing, we set an IP packet as the granularity of FastTraffic, truncate the informative parts in packets as inputs, and utilize a text-like packet tokenization method. For a lightweight and effective model, we propose an N-gram feature embedding method to represent structured and sequential features of packets and design a three-layer MLP to complete fast classification. We compare FastTraffic with eight state-of-the-art ETC methods on three public benchmark datasets. The experimental results show that FastTraffic obtains better classification performance than the other seven methods with only 0.43M model parameters. Besides, it can also achieve high throughput on low-configuration devices and consume a small amount of computing and storage resources. Therefore, FastTraffic is a lightweight ETC method capable of large-scale deployment on Internet devices.