ReXNet：融合不确定性量化与可解释性的空天安全可信框架

刘壮; 陈雨然; 张嘉桐; 蒋雨静; 汪旭晖

doi:10.11999/JEIT251159

ReXNet：融合不确定性量化与可解释性的空天安全可信框架

doi: 10.11999/JEIT251159 cstr: 32379.14.JEIT251159

1.
东北财经大学金融科技学院大连 116025
2.
东北财经大学管理科学与工程学院大连 116025
3.
东北财经大学工商管理学院大连 116025

基金项目: 国家自然科学基金委员会重点专项(72442025)

详细信息

作者简介:
刘壮：男，副教授、博士/硕士研究生导师，研究方向为图像处理与智能识别、空天信息网络、智能与协同控制、机器学习等

陈雨然：女，研究方向为机器学习、人工神经网络与计算等

张嘉桐：女，研究方向为机器学习、人工神经网络与计算等

蒋雨静：女，研究方向为机器学习、人工神经网络与计算等

汪旭晖：男，教授、博士/硕士研究生导师，研究方向为专家系统

通讯作者:
通信作者*：陈雨然　yuranchen06@gmail.com

中图分类号: TP393.081
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出版历程
- 收稿日期: 2025-10-31
- 修回日期: 2025-01-27
- 录用日期: 2026-01-30
- 网络出版日期: 2026-02-12

ReXNet: A Trustworthy Framework for Space-Air Security Integrating Uncertainty Quantification and Explainability

1.
Dongbei University of Finance and Economics, Dalian 116025, China

Funds: Project supported by Key Research Program of the National Natural Science Foundation of China (Grant No. 72442025)

摘要

摘要: 随着空天地一体化网络日益发展，成为国家战略前沿，其深度融合的卫星遥感、导航定位和通信应用，均对人工智能的可靠性与透明度提出了严苛要求。特别地，空天信息系统面临着物理层、网络层到应用层的复合式安全挑战，在这些高风险敏感性场景中，发展高稳健性与可信度的智能检测技术已成为当务之急。为应对这一挑战，该文提出了一个新颖的可信人工智能框架ReXNet。该框架深度整合了不确定性量化(UQ)与可解释人工智能(XAI)技术，并允许灵活替换骨干模型，以适配多样化的空天安全任务。通过在UAV-GCS入侵检测、C-MAPSS故障诊断及ADS-B注入攻击等空天地三层典型安全场景数据集上的系统性实验验证，ReXNet框架在保持高精度异常检测性能的同时，能有效量化预测置信度、识别模型知识边界外的未知样本，并为决策提供逻辑一致且可追溯的归因解释。该框架的模块化与灵活性创新，为解决人工智能在安全关键系统中的应用瓶颈提供了有效的技术路径。通过系统性地提升模型的可靠性与透明度，本研究旨在推动智能检测技术在空天安全领域的应用范式从追求单一的“高精度”向兼顾“高可信”转变，显著增强了其场景适用性与整体可信度。
- 空天地一体化网络 /
- 空天安全 /
- 可信人工智能 /
- 不确定性量化 /
- 可解释性
Abstract: Objective The space-air-ground integrated network (SAGIN) has emerged as a new strategic infrastructure for national development, yet its security vulnerabilities are becoming increasingly prominent. Each layer of the SAGIN, namely the physical, network, and application layers, faces distinct security challenges that require targeted solutions. Given the high demand for both predictive accuracy and decision transparency in aerospace scenarios, there is an urgent need for more robust, reliable, and interpretable intelligent techniques to ensure network security and trustworthiness. Methods This study proposes a detection framework that deeply integrates Uncertainty Quantification (UQ) and Explainable Artificial Intelligence (XAI). On the front end, the framework employs a Bayesian deep learning approach based on Monte Carlo Dropout, enabling probabilistic modeling of predictions. This allows for the separation and quantification of epistemic uncertainty and aleatoric uncertainty, thereby improving model reliability. On the back end, SHAP and LIME are incorporated to provide clear and trustworthy feature attribution for each model decision, enhancing interpretability and transparency. Moreover, the middle layer of the framework allows flexible substitution of specific deep learning backbones to adapt to various space and aerospace application scenarios. Results and Discussions Extensive experiments were conducted on representative space–air security datasets, including UAV swarm fault detection, ADS-B injection attacks , and network fraud detection . The results demonstrate that the proposed framework achieves high-precision anomaly detection while effectively evaluating prediction confidence and identifying unknown samples beyond the model’s knowledge boundaries. Furthermore, the framework provides logically consistent and traceable explanations for model decisions, offering both interpretive depth and operational reliability. These results confirm that the joint use of UQ and XAI significantly enhances the robustness and trustworthiness of intelligent models in aerospace security applications. Conclusions This study systematically enhances the reliability and transparency of anomaly detection models in the space-air domain, marking a paradigm shift in the application of artificial intelligence from solely pursuing high accuracy to emphasizing high trustworthiness. Future work will focus on advancing the framework toward real-world deployment, emphasizing real-time processing, lightweight implementation, and resource-constrained environments such as on-orbit or onboard systems. These efforts aim to enable SAGINs to operate with greater security, autonomy, and efficiency, contributing to the sustainable and intelligent development of future space–air information networks.
- Space-Air-Ground Integrated Network (SAGIN) /
- Aerospace Security /
- Trustworthy AI /
- Uncertainty Quantification (UQ) /
- Explainable AI(XAI)

HTML全文

图 1 ReXNet框架结构层级概述图

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图 2 不确定性量化分析

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图 3 SHAP全局特征重要性排序与影响分布图

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图 4 SHAP依赖图：bwd_pkt_len_mean (排名第一) 特征的详细效应

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图 5 (a)攻击样本的SHAP瀑布图；(b)同一样本的LIME归因图

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图 6 最重要特征的PDP-UQ协同分析图

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图 7 ReXNet框架的扩展性可解释分析

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表 1 不同模型在 UAV-GCS-IDS 数据集上的性能对比

模型类别	模型架构	Accuracy	F1-Score
基准模型
	TabNet	0.909	0.952
	XGBoost	0.929	0.962
	Transformer	0.910	0.953
骨干模型
	DNN	0.912	0.954
	CNN	0.907	0.950
	LSTM	0.903	0.948
	ResNet	0.911	0.953
	GatedNet	0.913	0.955
ReXNet 模式一(EU 量化)
	B-DNN	0.919	0.957
	B-CNN	0.911	0.953
	B-LSTM	0.909	0.951
	B-ResNet	0.912	0.961
	B-GatedNet	0.912	0.954
ReXNet 模式二(完整 UQ)
	Full-DNN	0.925	0.960
	Full-CNN	0.917	0.956
	Full-LSTM	0.913	0.954
	Full-ResNet	0.933	0.965
	Full- GatedNet	0.927	0.961

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表 2 不同模型在SAGIN不同层次上的F1-Score性能对比

模型类别	模型架构	数据集
		物理层	网络层		应用层
		C-MAPSS	T-ITS	ADS-B	GUIDE
基准模型
	TabNet	0.836	0.924	0.986	0.772
	XGBoost	0.877	0.937	0.985	0.917
	Transformer	0.689	0.919	0.653	0.744
骨干模型
	DNN	0.940	0.938	0.971	0.915
	CNN	0.938	0.945	0.977	0.913
	LSTM	0.941	0.950	0.984	0.918
	ResNet	0.942	0.951	0.979	0.921
	GatedNet	0.940	0.949	0.967	0.919
ReXNet 模式一(EU 量化)
	B-DNN	0.959	0.941	0.977	0.922
	B-CNN	0.955	0.948	0.978	0.920
	B-LSTM	0.957	0.954	0.985	0.925
	B-ResNet	0.960	0.954	0.982	0.928
	B-GatedNet	0.958	0.952	0.981	0.926
ReXNet 模式二(完整 UQ)
	Full-DNN	0.986	0.946	0.979	0.931
	Full-CNN	0.985	0.952	0.980	0.929
	Full-LSTM	0.987	0.956	0.986	0.934
	Full-ResNet	0.990	0.957	0.989	0.938
	Full-GatedNet	0.988	0.955	0.983	0.936

下载: 导出CSV

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