Sessionvideo: A Novel Approach for Encrypted Traffic Classification via 3D-CNN Model

Today encrypted traffic has been used widely on the internet, such as HTTPS and SSH. Network traffic classification plays an important role in network resource management and cyberspace security, and previous machine learning methods face the challenge of identifying and classifying the encrypted traffic. In this paper, we propose a deep learning method based on a 3D convolutional neural network (3D-CNN), called SessionVideo. The new approach integrates traffic payload and time feature extraction for application classification. Specifically, our proposed scheme converts the raw traffic into a simple grayscale video, which enables the model to capture the temporal characteristics of the traffic. To verify the effectiveness of this scheme, we build a traffic dataset of 20 applications. The experimental results demonstrate that our 3D-CNN model achieves an accuracy of 97.89% and a weighted average precision of 97.96%. Further, the evaluation metrics of the 3D-CNN model significantly outperform 1D-CNN and 2D-CNN models in the comparison experiments.