广西师范大学学报（自然科学版） ›› 2021, Vol. 39 ›› Issue (2): 21-31.doi: 10.16088/j.issn.1001-6600.2020082601

• CCIR2020 • 上一篇    下一篇

一种空间信息网络抗毁分析的新方法

禚明1, 刘乐源1, 周世杰1*, 杨鹏1,2, 万思敏2   

  1. 1.电子科技大学 信息与软件工程学院, 四川 成都 610054;
    2.电子科技大学 数学科学学院, 四川 成都 611731
  • 收稿日期:2020-08-26 修回日期:2020-09-22 出版日期:2021-03-25 发布日期:2021-04-15
  • 通讯作者: 周世杰(1970—),男,四川自贡人,电子科技大学教授,博导。E-mail:sjzhou@uestc.edu.cn
  • 基金资助:
    四川省重大科技专项(2018GZDZX0006,2018GZDZX0007)

A New Method for Invulnerability Analysis of Spatial Information Networks

ZHUO Ming1, LIU Leyuan1, ZHOU Shijie1*, YANG Peng1,2, WAN Simin2   

  1. 1. School of Information and Software Engineering, University of Science and Technology of China, Chengdu Sichuan 610054, China;
    2. School of Mathematical Sciences, University of Science and Technology of China, Chengdu Sichuan 611731, China
  • Received:2020-08-26 Revised:2020-09-22 Online:2021-03-25 Published:2021-04-15

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摘要： 在下一代网络中,空间信息网络将在提供长距离、全覆盖的互联网服务方面发挥越来越重要的作用。未来的大多数网络将是混合型的——通过卫星链路将太空、临近空间和陆地上的节点连接起来。安全是空间信息网络的一个重要问题,因为此类网络容易受到大量攻击,包括窃听、会话劫持、数据损坏和分割攻击等。本文讨论了空间信息网络中可能发生的各种安全攻击,并概述了现有的网络抗毁性分析的不同解决方案。以分割攻击为背景,提出了一种基于复杂网络节点重要性和图卷积网络节点分类的网络抗毁性评估方案。通过简单网络和真实的空间信息网络进行抗毁性评估实验,结果表明,提出的评估方案具有良好的区分度和准确性。

关键词: 空间信息网络, 节点重要性, 图卷积网络, 抗毁性

Abstract: In next-generation networks, Spatial Information Networks (SINs) are expected to play an increasingly important role in providing Internet services over long distances and full coverage in an efficient manner. Most future networks will be hybrid-connecting nodes in space, near space, and on land through satellite links. Security is an essential concern in such networks since it is susceptible to a host of attacks, including eavesdropping, session hijacking, data corruption, and split attack. In this article, various security attacks that are possible in spatial information networks are addressed and different solutions proposed to existing invulnerability analysis in these networks are outlined. Setting in the split attack, a scheme is pointed out to evaluate network invulnerability based on the importance of the node in the complex network and graph convolutional networks classification nodes. Through experiments on the simple network and real spatial information networks, it is verified that the suggested approach has good discrimination and accuracy.

Key words: spatial nformation networks, node importance, graph convolutional networks, invulnerability

中图分类号: 

  • TP391
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