NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models
<|reference_start|>NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models: 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.<|reference_end|>
@article{qian2024netbench:,
    title={NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark
  Dataset for Foundation Models},
    author={Chen Qian, Xiaochang Li, Qineng Wang, Gang Zhou, Huajie Shao},
    journal={arXiv preprint arXiv:2403.10319},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10319},
    primaryClass={cs.NI cs.CR}
}