NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models

TL;DR

NetBench is a large, comprehensive dataset designed to evaluate machine learning models, especially foundation models, for network traffic classification and generation, promoting fair comparison and advancing research.

Contribution

The paper introduces NetBench, a large-scale benchmark dataset combining multiple datasets and tasks for network traffic analysis, specifically tailored for foundation models.

Findings

Foundation models outperform traditional deep learning in traffic classification.

NetBench enables fairer comparisons across different network traffic analysis methods.

Benchmark covers 20 diverse tasks, including classification and generation.

Abstract

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.