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
[1] PADMAVATHI G,SHANMUGAPRIYA D.A survey of attacks,security mechanisms and challenges in wireless sensor networks[J].International Journal of Computer Science and Information Security,2009,4(1/2):1-9.
[2] RYZHOV Y,SAKOVYCH L,VANKEVYCH P,et al.Optimization of requirements for measuring instruments at metrological service of communication tools[J].Measurement,2018,123:19-25.DOI:10.1016/j.measurement.2018.03.055.
[3] 张殷,肖先勇,陈晶,等.信息网受损对电网过载主导型连锁故障的影响[J].电力系统自动化,2017,41(13):14-21.
[4] 谭跃进,吴俊,邓宏钟.复杂网络中节点重要度评估的节点收缩方法[J].系统工程理论与实践,2006,26(11):79-83,102.
[5] 吴俊,谭跃进,邓宏钟,等.考虑级联失效的复杂负载网络节点重要度评估[J].小型微型计算机系统,2007,28(4):627-630.
[6] 刘艳,顾雪平.基于节点重要度评价的骨架网络重构[J].中国电机工程学报,2007,27(10):20-27.
[7] WEST D B.Introduction to graph Theory[M].2nd ed.Upper Saddle River:Prentice Hall,2001.
[8] CALLAWAY D S,NEWMAN M E J,STROGATZ S H,et al.Network robustness and fragility:percolation on random graphs[J].Physical Review Letters,2000,85(25):5468-5471.DOI:10.1103/PhysRevLett.85.5468.
[9] BORGATTI S P.Centrality and network flow[J].Social Networks,2005,27(1):55-71.DOI:10.1016/j.socnet.2004.11.008.
[10] BRANDES U.A faster algorithm for betweenness centrality[J].Journal of Mathematical Sociology,2001,25(2):163-177.DOI:10.1080/0022250X.2001.9990249.
[11] 张凯,马英红.基于网络结构的节点中心性排序优化算法[J].计算机应用研究,2016,33(9):2596-2600,2605.
[12] BUDANITSKY A,HIRST G.Evaluating WordNet-based measures of semantic distance[J].Computational Linguistics,2006,32(1):13-47.
[13] NEWMAN M E J.Mathematics of networks[M]//VERNENGO M,CALDENTEY E P,ROSSER JR B J.The New Palgrave Dictionary of Economics.London:Palgrave Macmillan,2016:1-8.DOI:10.1057/978-1-349-95121-5_2565-1.
[14] NEGRE C F A,MORZAN U N,HENDRICKSON H P,et al.Eigenvector centrality for characterization of protein allosteric pathways[J].Proceedings of the National Academy of Sciences of the United States of America,2018,115(52):E12201-E12208.DOI:10.1073/pnas.1810452115.
[15] KITSAK M,GALLOS L K,HAVLIN S,et al.Identification of influential spreaders in complex networks[J].Nature Physics,2010,6(11):888-893.DOI:10.1038/nphys1746.
[16] ESTRADA E,RODRÍGUEZ-VELÁZQUEZ J A.Subgraph centrality in complex networks[J].Physical Review E Statistical Nonlinear and Soft Matter Physics,2005,71(5):056103.DOI:10.1103/PhysRevE.71.056103.
[17] FRANK H,FRISCH I T.Analysis and design of survivable networks[J].IEEE Transactions on Communication Technology,1970,18(5):501-519.DOI:10.1109/TCOM.1970.1090419.
[18] WANG B,TANG H W,GUO C H,et al.Optimization of network structure to random failures [J].Physica A:Statistical Mechanics and its Applications,2006,368(2):607-614.
[19] ALBERT R,JEONG H,BARABÁSI A L.Error and attack tolerance of complex networks[J].2000,406:378-382.
[20] 郭伟.野战地域通信网可靠性的评价方法[J].电子学报,2000,28(1):3-6.
[21] 饶育萍,林竞羽,侯德亭.基于最短路径数的网络抗毁评价方法[J].通信学报,2009,30(4):113-117.
[22] 吴俊,谭索怡,谭跃进,等.基于自然连通度的复杂网络抗毁性分析[J].复杂系统与复杂性科学,2014,11(1):77-86.DOI:10.13306/j.1672-3813.2014.01.010.
[23] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[EB/OL].(2014-06-10)[2020-08-26].https://arxiv.org/pdf/1406.2661.pdf.
[24] DUVENAUD D,MACLAURIN D,AGUILERA-IPARRAGUIRRE J,et al.Convolutional networks on graphs for learning molecular fingerprints[EB/OL].(2015-11-03)[2020-08-26].https://arxiv.org/pdf/1509.09292.pdf.
[25] LI Y J,ZEMEL R,BROCKSCHMIDT M,et al.Gated graph sequence neural networks[EB/OL].(2017-09-22)[2020-08-26].https://arxiv.org/pdf/1511.05493.pdf.
[26] JAIN A,ZAMIR A R,SAVARESE S,et al.Structural-RNN:deep learning on spatio-temporal graphs[EB/OL].(2016-04-11)[2020-08-26].https://arxiv.org/pdf/1511.05298.pdf.
[27] BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral networks and locally connected networks on graphs[EB/OL].(2014-05-21)[2020-08-26].https://arxiv.org/pdf/1312.6203.pdf.
[28] HENAFF M,BRUNA J,LECUN Y.Deep convolutional networks on graph-structured Data[EB/OL].(2015-06-16)[2020-08-26].https://arxiv.org/pdf/1506.05163.pdf.
[29] DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[EB/OL].(2017-02-05)[2020-08-26].https://arxiv.org/pdf/1606.09375.pdf.
[30] KIPF T N,WELLING M.Semi-supervised classification with graph convolutional networks[EB/OL].(2017-02-22)[2020-08-26].https://arxiv.org/pdf/1609.02907.pdf.
[31] 杨红娃,王敏,潘高峰.基于网络割裂度的优化链路攻击方法[J].通信对抗,2010(4):39-42.
[32] 王彦鹏.卫星网络拓扑的抗毁性研究[D].大连:大连理工大学,2018.
[33] LEISERSON C E.Fat-trees:universal networks for hardware-efficient supercomputing[J].IEEE Transactions on Computers,1985,C-34(10):892-901.DOI:10.1109/TC.1985.6312192.
[34] YANG Z L,COHEN W W,SALAKHUTDINOV R.Revisiting semi-supervised learning with graph embeddings[EB/OL].(2016-05-26)[2020-08-26].https://arxiv.org/pdf/1603.08861.pdf.
[35] BELKIN M,NIYOGI P,SINDHWANI V.Manifold regularization:a geometric framework for learning from labeled and unlabeled examples[J].Journal of Machine Learning Research,2006,7:2399-2434.
[36] PEROZZI B,AL-RFOU R,SKIENA S.DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.New York,NY:Association for Computing Machinery,2014:701-710.DOI:10.1145/2623330.2623732.
[37] ZHU X J,GHAHRAMANI Z,LAFFERTY J.Semi-supervised learning using gaussian fields and harmonic functions[C]//Proceedings of the Twentieth International Conference on Machine Learning(ICML 2003).Palo Alto,CA:AAAI Press,2003:912-919.
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