NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models
Chen Qian, Xiaochang Li, Qineng Wang, Gang Zhou, Huajie Shao

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…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Software System Performance and Reliability · Traffic Prediction and Management Techniques
