Network configuration translation methods under low-resource scenarios

SHI Hongwei; SHI Jingwen; HUANG Tao

doi:10.11959/j.issn.1000-0801.DXKX260165

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Network configuration translation methods under low-resource scenarios

更新时间：2026-07-08

- Network configuration translation methods under low-resource scenarios
- Telecommunications Science (2026)
- 作者机构：
  
  1.东南大学网络空间安全学院，江苏南京 211189
  2.紫金山实验室未来网络研究中心，江苏南京 211111
  3.北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The Industrial Internet Innovation and Development Project of Ministry of Industry and Information Technology(TC240AAKM-137);The National Engineering Research Center for Next Generation Internet Broadband Service Applications Project of China United Network Communications Group Co., Ltd(2025-STHZ-ZJSSYS-001)
- DOI：10.11959/j.issn.1000-0801.DXKX260165
  CLC： TP393
- Received：11 March 2026，
  
  Revised：2026-06-02，
  
  Accepted：08 July 2026，
- 稿件说明：
移动端阅览
SHI Hongwei, SHI Jingwen, HUANG Tao. Network configuration translation methods under low-resource scenarios[J/OL]. Telecommunications Science, 2026.
DOI：

SHI Hongwei, SHI Jingwen, HUANG Tao. Network configuration translation methods under low-resource scenarios[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260165.

摘要

针对跨厂商网络设备配置转换场景中低语料条件下转换准确性与稳定性不足的问题，提出了一种基于Transformer架构的网络配置转换模型（network configuration translation model，NetConfigTM）。该方法融合网络配置领域特性，构建面向配置语义的训练机制，通过对有限配置语料进行扩展与建模，在低标注条件下实现有效特征学习。进一步引入掩码语言建模、去噪自编码与有监督微调相结合的多阶段训练策略，增强模型对跨厂商配置语法结构与语义映射关系的建模能力。基于脱敏网络配置数据开展实验验证，结果表明，该方法在多类典型配置场景下能够保持稳定、可靠的转换准确性，并可将人工方式下天级的配置转换过程缩短至分钟级，验证了其在跨厂商网络环境中开展自动化配置转换的工程可行性。

Abstract

To address the insufficient accuracy and stability of network configuration translation under low-resource scenarios in cross-vendor environments

a Transformer-based network configuration translation model (NetConfigTM) was proposed. The model integrated domain characteristics of network configurations and constructed a configuration-semantic-oriented training mechanism. By expanding and modeling limited configuration corpora

effective feature learning was realized under low-label conditions. Furthermore

a multi-stage training strategy combining masked language modeling

denoising autoencoding

and supervised fine-tuning was introduced to enhance the modeling capability of cross-vendor configuration syntax structures and semantic mapping relationships. Experiments conducted on desensitized network configuration data demonstrated that the proposed method maintained stable and reliable translation accuracy in multiple typical configuration scenarios and reduced the configuration translation time from days of manual operation to minutes

verifying its engineering feasibility for automated configuration translation in cross-vendor network environments.

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Keywords

references

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