Ptu: Pre-Trained Model for Network Traffic Understanding

Network traffic understanding is crucial to providing high-quality network services and protecting network security. However, due to the growing complexity of networks and the rising proportion of encrypted traffic, existing methods for network traffic understanding face severe challenges. Traditional approaches rely on manually designed features or require a large amount of labeled data, while pre-trained models offer new possibilities. Nevertheless, existing pre-trained models have the following limitations: (1) Their inputs only contain features from the packet content, neglecting temporal information about network dynamics. (2) Their pre-training targets only focus on static characteristics of the data stream without understanding the process of the network transmission. This paper presents the Pre-trained model for network Traffic Understanding (PTU), an innovative model that employs self-supervised pre-training to address the challenges of network traffic understanding. In PTU, we design a traffic representation scheme that integrates static packet content and network dynamics into a unified input space. Furthermore, we propose a pre-training method that includes four tailored pre-training targets. This approach enables PTU to capture both static and dynamic characteristics of network traffic from massive amounts of unlabeled data, thereby achieving enhanced performance in downstream tasks through fine-tuning. Extensive experiments confirm PTU's state-of-the-art (SOTA) performance. In traffic classification tasks, PTU achieves an F1 score of over $\mathbf{0. 9 9}$ and secures a more than $\mathbf{1 0 \%}$ improvement in accuracy in the most challenging task of encrypted application classification.