MT-CNN: A Classification Method of Encrypted Traffic Based on Semi-Supervised Learning
KaiChao Shi, Yong Zeng, Baihe Ma, Zhihong Liu, Jianfeng Ma · 2023
Deep learning methods have become the preferred solution for encrypted traffic classification. However, the application of neural networks in encrypted traffic classification has encountered the following limitations: 1) Deep learning models have dependencies on large-scale and well-labeled datasets. 2) most deep learning models have high hardware requirements and require a large amount of CPU and GPU for computation. These limitations seriously hinder the development of encrypted traffic research. In this paper, we propose a new lightweight semi-supervised learning classifier to solve these problems. To reduce the dependence of the model on CPU and GPU, we have designed a lightweight encrypted traffic classifier based on CNN(Convolutional Neural Networks). It can run on raspberry pi with low hardware requirements. Then we combine the classifier with the Mean Teacher framework, which we call MT-CNN. By using the semi-supervised learning framework, we successfully reduced the number of labeled samples during model training. To fully preserve traffic information, we convert traffic data into grayscale images as input. We used a small-scale dataset for experiments on raspberry pi. The experimental results showed that the accuracy of MT-CNN still reached 96.83% even when only 5% of the labeled data was used.