  In computer networking, network traffic refers to the amount of data
transmitted in the form of packets between internetworked computers or
Cyber-Physical Systems. Monitoring and analyzing network traffic is crucial for
ensuring the performance, security, and reliability of a network. However, a
significant challenge in network traffic analysis is to process diverse data
packets including both ciphertext and plaintext. While many methods have been
adopted to analyze network traffic, they often rely on different datasets for
performance evaluation. This inconsistency results in substantial manual data
processing efforts and unfair comparisons. Moreover, some data processing
methods may cause data leakage due to improper separation of training and
testing data. To address these issues, we introduce the NetBench, a large-scale
and comprehensive benchmark dataset for assessing machine learning models,
especially foundation models, in both network traffic classification and
generation tasks. NetBench is built upon seven publicly available datasets and
encompasses a broad spectrum of 20 tasks, including 15 classification tasks and
5 generation tasks. Furthermore, we evaluate eight State-Of-The-Art (SOTA)
classification models (including two foundation models) and two generative
models using our benchmark. The results show that foundation models
significantly outperform the traditional deep learning methods in traffic
classification. We believe NetBench will facilitate fair comparisons among
various approaches and advance the development of foundation models for network
traffic. Our benchmark is available at https://github.com/WM-JayLab/NetBench.
