Unmanned aerial vehicle system security cyber range construction and attack-defense validation design

Li Yujia; Zhang Hui; Duo Bin; Wang Zibin; Zhou Rang; Li Dongfen

doi:10.11959/j.issn.2096-109x.2026010

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Unmanned aerial vehicle system security cyber range construction and attack-defense validation design

Papers | 更新时间：2026-05-26

- Unmanned aerial vehicle system security cyber range construction and attack-defense validation design
- Chinese Journal of Network and Information Security Vol. 12, Issue 1, Pages: 54-73(2026)
- 作者机构：
  
  1.成都理工大学计算机与网络安全学院，四川成都 610059
  2.北京信息系统安全技术重点实验室北京100093
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62172060);Innovative Experiment Project of Colleges and Universities in Sichuan Province “Innovative Experiment on Security of Intelligent Unmanned Systems”, Higher Education Talent Training Quality and Teaching Reform Project of Chengdu University of Technology
- DOI：10.11959/j.issn.2096-109x.2026010
  CLC： TP393
- Received：13 August 2025，
  
  Revised：2025-12-13，
  
  Published：25 February 2026
- 稿件说明：
移动端阅览
李宇佳, 张辉, 多滨, 等. 无人机系统安全靶场构建与攻防验证设计[J]. 网络与信息安全学报, 2026, 12(1): 54-73.

Li Yujia, Zhang Hui, Duo Bin, et al. Unmanned aerial vehicle system security cyber range construction and attack-defense validation design[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 54-73.
李宇佳, 张辉, 多滨, 等. 无人机系统安全靶场构建与攻防验证设计[J]. 网络与信息安全学报, 2026, 12(1): 54-73. DOI： 10.11959/j.issn.2096-109x.2026010.

Li Yujia, Zhang Hui, Duo Bin, et al. Unmanned aerial vehicle system security cyber range construction and attack-defense validation design[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 54-73. DOI： 10.11959/j.issn.2096-109x.2026010.

摘要

无人机技术当前被广泛应用于农业、物流、环境监测等领域，其安全问题逐渐成为研究的焦点。但当前无人机安全研究中的仿真测试平台存在无法系统有效地开展威胁分析并识别攻击类型与风险点、难以生成高质量攻击数据集以支撑入侵检测模型训练、无法实现攻防迭代验证等问题。针对上述问题，提出了一套无人机攻防闭环验证的实验框架，设计了一个包含基础设施层、模拟层、通信层、应用层和智能调度层5层架构的新型人工智能辅助无人机系统安全靶场。借助靶场提供的仿真测试环境，构建了从Wi-Fi发现、密码破解到指令欺骗的综合攻击场景。利用在该攻击场景下生成的实验数据，设计了一种基于单类支持向量机的入侵检测系统，经攻防迭代调整并不断优化实验参数，该入侵检测系统在指令欺骗攻击检测任务中的检测精确率由85.6%提升至93.8%，相比基于经典局部离群因子算法构建的模型，优化后的单类支持向量机模型检测精确率在多种攻击场景下均更具优势，证实了所提出的无人机系统安全靶场在威胁分析、数据集构建和攻防闭环验证方面的系统性、完备性和有效性。

Abstract

Unmanned aerial vehicle (UAV) technology is currently widely applied in fields such as agriculture

logistics and environmental monitoring

and its security issues have gradually become a research focus. However

in current UAV security research

existing simulation and testing platforms have problems: effective and systematic threat analysis cannot be carried out to identify attack types and risk points; high-quality attack datasets are difficult to be generated to support the training of intrusion detection models; “attack-defense iterative verification” cannot be realized

that is

the model is tested by attacks and then further optimized according to the results.To address the above problems

an experimental framework for UAV attack-defense closed-loop verification was proposed. A new artificial intelligence-assisted UAV system security testbed with a five-layer architecture (infrastructure layer

simulation layer

communication layer

application layer and intelligent scheduling layer) was designed and implemented. With the simulation test environment provided by the testbed

a comprehensive attack scenario covering Wi-Fi discovery

password cracking to command spoofing was constructed. Using the experimental data generated in this attack scenario

an intrusion detection system based on one-class support vector machine was designed. After iterative adjustment of attack and defense and continuous optimization of experimental parameters

the detection precision of the intrusion detection system for command spoofing attacks was improved from 85.6% to 93.8%. Compared with the model constructed based on the classic local outlier factor algorithm

the optimized one-class support vector machine model has more advantages in detection precision under various attack scenarios

which verifies that the proposed UAV system security testbed is systematic

complete and effective in threat analysis

dataset construction and attack-defense closed-loop verification.

关键词

Keywords

references

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