基于混合知识蒸馏的无人机网络轻量级入侵检测方法

张礼哲; 张鹏宇; 吴志军

doi:10.11959/j.issn.1000-0801.DXKX260281

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基于混合知识蒸馏的无人机网络轻量级入侵检测方法

专题:低空智联网 | 更新时间：2026-09-22

- 基于混合知识蒸馏的无人机网络轻量级入侵检测方法
- A Lightweight Intrusion Detection Method for UAV Networks Based on Hybrid Knowledge Distillation
- 电信科学 2026年
- 作者机构：
  
  中国民航大学安全科学与工程学院，天津东丽 300300
- 作者简介：
  
  张礼哲（1986- ），男，中国民航大学安全科学与工程学院讲师，主要研究方向为低空智联网安全、数据安全与隐私保护。
  张鹏宇（2002- ），男，中国民航大学安全科学与工程学院硕士生，主要研究方向为无人机网络入侵检测。
  吴志军（1965- ），男，中国民航大学安全科学与工程学院教授，主要研究方向为航空电信网及信息安全、云计算与大数据安全。
- 基金信息：
  
  国家自然科学基金(62571531)
- DOI：10.11959/j.issn.1000-0801.DXKX260281
  中图分类号： TP393
- 收稿：2026-04-30，
  
  修回：2026-08-09，
  
  录用：2026-08-25，
- 稿件说明：
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张礼哲, 张鹏宇, 吴志军. 基于混合知识蒸馏的无人机网络轻量级入侵检测方法[J/OL]. 电信科学, 2026.

ZHANG Lizhe, ZHANG Pengyu, WU Zhijun. A Lightweight Intrusion Detection Method for UAV Networks Based on Hybrid Knowledge Distillation[J/OL]. Telecommunications Science, 2026.
张礼哲, 张鹏宇, 吴志军. 基于混合知识蒸馏的无人机网络轻量级入侵检测方法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260281.

ZHANG Lizhe, ZHANG Pengyu, WU Zhijun. A Lightweight Intrusion Detection Method for UAV Networks Based on Hybrid Knowledge Distillation[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260281.

摘要

随着低空经济的快速发展，无人机网络面临的安全威胁日益增多，传统的入侵检测方法难以直接部署到无人机端。针对这一问题提出了一种基于混合知识蒸馏的无人机网络轻量级入侵检测方法。该方法采用双向门控循环单元与Transformer串行结合的方式，构建高性能教师模型，然后使用混合知识蒸馏策略，获得具有高检测性能的无人机网络轻量级入侵检测学生模型。在UAV-NIDD数据集上的实验结果表明，学生模型在参数量和模型大小相较于教师模型均降低约98%的情况下，仍取得了0.9616的宏平均F1值，并在树莓派端实现了1.198ms的推理时延。

Abstract

With the rapid development of the low-altitude economy

security threats facing Unmanned Aerial Vehicle (UAV) networks have increased

and traditional intrusion detection methods are difficult to deploy directly on UAV terminals. To address this problem

a lightweight intrusion detection method for UAV networks based on hybrid knowledge distillation was proposed. This method adopted the serial combination of Bidirectional Gated Recurrent Unit and Transformer to construct a high-performance teacher model

and then used a hybrid knowledge distillation strategy to obtain a lightweight intrusion detection student model for UAV networks with high detection performance. Experimental results on the UAV-NIDD dataset show that

while the number of parameters and model size of the student model are both reduced by approximately 98% compared with those of the teacher model

the student model still achieves a macro-average F1 score of 0.9616 and an inference latency of only 1.198 ms on the Raspberry Pi.

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references

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