MT-CNN: A Classification Method of Encrypted Traffic Based on Semi-Supervised Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2024, 00, pp. 7538-7543
- Issue Date:
- 2024-02-26
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1712174.pdf | Published version | 4.69 MB |
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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
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