Research on airborne network intrusion detection combining adaptive data augmentation and ensemble learning

Zhou Maohui; Liu Wenqi; Li Yanjun; Gong Yishu

doi:10.11959/j.issn.1000-0801.2026050

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Research on airborne network intrusion detection combining adaptive data augmentation and ensemble learning

Research and Development | 更新时间：2026-04-08

- Research on airborne network intrusion detection combining adaptive data augmentation and ensemble learning
- Telecommunications Science Vol. 42, Issue 3, Pages: 113-122(2026)
- 作者机构：
  
  1.南京航空航天大学民航学院，江苏南京 210016
  2.新疆通达低空科技有限公司，新疆石河子 832000
- 作者简介：
- 基金信息：
  
  The Project of Jiangsu Province Graduate Research and Practical Innovation Program(KYCX25_0620);Supported by Science and Technology Program of XPCC(2025AB070;2025AB068)
- DOI：10.11959/j.issn.1000-0801.2026050
  CLC： TN915.08;TP309
- Received：23 July 2025，
  
  Revised：2025-11-25，
  
  Accepted：25 November 2025，
  
  Published：20 March 2026
- 稿件说明：
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周茂辉,刘文琪,李艳军等.结合自适应数据增强和集成学习的机载网络入侵检测研究[J].电信科学,2026,42(03):113-122.

Zhou Maohui,Liu Wenqi,Li Yanjun,et al.Research on airborne network intrusion detection combining adaptive data augmentation and ensemble learning[J].Telecommunications Science,2026,42(03):113-122.
周茂辉,刘文琪,李艳军等.结合自适应数据增强和集成学习的机载网络入侵检测研究[J].电信科学,2026,42(03):113-122. DOI： 10.11959/j.issn.1000-0801.2026050.

Zhou Maohui,Liu Wenqi,Li Yanjun,et al.Research on airborne network intrusion detection combining adaptive data augmentation and ensemble learning[J].Telecommunications Science,2026,42(03):113-122. DOI： 10.11959/j.issn.1000-0801.2026050.

摘要

机载网络入侵检测可能面临异常样本稀缺和数据分布不平衡的双重挑战，传统方法难以同时保证检测精度和泛化能力。为此，结合多视图对比稀疏自编码器（multi-view contrastive sparse autoencoder，MCSAE）的数据增强方法，提出一种改进分层抽样集成学习的联合优化方法。首先，针对异常样本缺失问题，设计MCSAE，通过多视图数据增强和对比学习策略，在稀疏自编码器框架下学习更具判别性的潜在表示，并利用重输入对比机制优化异常样本生成质量，有效缓解数据稀疏性带来的模型偏差。其次，针对类别不平衡问题，提出改进分层抽样策略，在传统分层抽样基础上引入全局特征保留机制，避免局部采样导致多数类分布失真，确保分类器能够学习数据的完整统计特性。最后，结合F1分数自适应加权集成学习，融合随机森林、长短期记忆（long short-term memory，LSTM）网络等多样化基分类器，动态调整模型权重，进一步提升对少数类攻击的检测能力。实验结果表明，相较于现有方法，所提方法在机载网络数据集上的召回率提升5.2%，F1分数提升3.7%，为复杂网络环境下的入侵检测提供了可靠解决方案。

Abstract

Airborne network intrusion detection may face the dual challenges of scarce abnormal samples and unbalanced data distribution. Traditional methods are difficult to simultaneously guarantee detection accuracy and generalization ability. Therefore

combined with the data augmentation method of multi-view contrastive sparse autoencoder (MCSAE)

a joint optimization method with improved hierarchical sampling ensemble learning was proposed. Firstly

for the problem of missing abnormal samples

a MCSAE was designed. Through multi-view data augmentation and contrastive learning strategies

a more discriminative latent representation was learned under the framework of sparse autoencoder

and the quality of abnormal sample generation was optimized by the re-input contrast mechanism

effectively alleviating the model deviation caused by data sparsity. Secondly

for the problem of class imbalance

an improved hierarchical sampling strategy was proposed. On the basis of traditional hierarchical sampling

a global feature retention mechanism was introduced to avoid the distortion of the majority class distribution caused by local sampling

ensuring that the classifier can learn the complete statistical characteristics of the data. Finally

combined with F1 score adaptive weighted ensemble learning

diverse base classifiers such as random forest and LSTM were integrated to dynamically adjust the model weights

further improving the detection ability for minority class attacks. The experimental results show that

compared with the existing methods on the airborne network dataset

the proposed method has a 5.2% increase in recall rate and a 3.7% increase in F1 score. This provides a reliable solution for intrusion detection in complex network environments.

关键词

Keywords

references

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