基于GCN-Transformer的空管系统网络威胁实体识别方法

刘超; 田继伟; 贾楠; 张欢乐; 王布宏; 刘洲洲

doi:10.11959/j.issn.1000-0801.DXKX260210

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基于GCN-Transformer的空管系统网络威胁实体识别方法

更新时间：2026-06-23

- 基于GCN-Transformer的空管系统网络威胁实体识别方法
- A GCN-Transformer method for network threat entity recognition in air traffic management systems
- 电信科学 2026年
- 作者机构：
  
  1.西安航空学院计算机与人工智能学院，陕西西安 710077
  2.中国人民解放军空军工程大学，陕西西安 710051
- 作者简介：
  
  田继伟，邮箱：tianjiwei2016@163.com
- 基金信息：
  
  国家自然科学基金资助项目(62472437)
- DOI：10.11959/j.issn.1000-0801.DXKX260210
  中图分类号： TP393.08;V355.1
- 收稿：2026-04-01，
  
  修回：2026-05-26，
  
  录用：2026-06-23，
- 稿件说明：
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刘超, 田继伟, 贾楠, 等. 基于GCN-Transformer的空管系统网络威胁实体识别方法[J/OL]. 电信科学, 2026.

LIU Chao, TIAN Jiwei, JIA Nan, et al. A GCN-Transformer method for network threat entity recognition in air traffic management systems[J/OL]. Telecommunications Science, 2026.
刘超, 田继伟, 贾楠, 等. 基于GCN-Transformer的空管系统网络威胁实体识别方法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260210.

LIU Chao, TIAN Jiwei, JIA Nan, et al. A GCN-Transformer method for network threat entity recognition in air traffic management systems[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260210.

摘要

随着空管系统数字化转型深入，网络安全风险持续加剧，威胁实体识别已成为构建网络安全知识图谱的关键基础。针对传统序列标注模型难以有效捕捉文本语法结构与语义依赖的问题，提出一种融合图卷积网络（graph convolutional network，GCN）与transformer的空管网络威胁实体识别方法。方法通过语法解析与词共现构建文本图，利用节点重要性排序与广度优先搜索生成语义子图并完成标准化；设计基于层次正则化的深度图卷积网络HR-DGCNN，引入父子标签相似性约束，增强层次化威胁实体识别能力；借助transformer编码器提取全局语义特征，结合条件随机场（conditional random field，CRF）完成最优实体序列解码。自建空管威胁语料库进行实验，模型F1值、精确率、召回率均优于BiLSTM-CRF、BERT-Tagging等基线模型。该模型为空管系统网络威胁实体识别提供了有效新思路，具备理论与应用价值。

Abstract

With the deepening digital transformation of air traffic management systems

cybersecurity risks are becoming increasingly prominent

and threat entity recognition has become a critical foundation for constructing cybersecurity knowledge graphs. To address the limitations of traditional sequence labeling models in capturing syntactic structures and semantic dependencies

this paper proposes a network threat entity recognition method for air traffic management systems that integrates graph convolutional networks (GCN) with a Transformer. Text graphs are constructed based on syntactic parsing and word co-occurrence

while semantic subgraphs are generated and standardized through node importance ranking and breadth-first search. A hierarchically regularized deep graph convolutional neural network (HR-DGCNN) is designed

incorporating parent-child label similarity constraints to enhance the recognition of hierarchical threat entities. In addition

a Transformer encoder is used to extract global semantic features

and a conditional random field (CRF) is employed to decode the optimal entity sequence. Experiments on a self-built ATC threat corpus show that the proposed model outperforms baseline models

including BiLSTM-CRF and BERT-Tagging

in terms of F1 score

precision

and recall. The results demonstrate that the proposed model provides an effective approach for network threat entity recognition in ATC systems and has both theoretical significance and practical value.

关键词

Keywords

references

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