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Hierarchy-entropy based method for command and control networks reconfiguration

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Abstract

Network reconfiguration is an important means of improving network invulnerability. However, most existing network reconfiguration methods fail to consider node importance, edge importance, and hierarchical characteristics, and the local and global information of command and control (C2) networks are difficult to satisfy comprehensively. Therefore, this study designed a hierarchy-entropy-based method for reconfiguring C2 networks. By combining hierarchical and operational link entropy, the probability of inter-node edge reconfiguration based on hierarchy entropy is proposed. Additionally, methods for calculating the node level-up, cross-level, and swap degrees, and a portfolio reconfiguration strategy are proposed. Finally, to validate the proposed method, a case study was simulated, and the repair probability, adjustable parameters, and reconfiguration effects of the different reconfiguration methods and modes were determined. The comparison results demonstrate that the proposed algorithm improves the reconfiguration effect and reduces the reconfiguration cost.

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References

  1. Ali O, Suna D, Serdar U (2019) The challenges in implementing future interoperable joint C4ISR systems. INCOSE Int Symp 29:1. https://doi.org/10.1002/j.2334-5837.2019.00663.x

    Article  Google Scholar 

  2. He HY, Zhu WX, Li RY, Deng QY (2020) An executable modeling and analyzing approach to C4ISR architecture. J Syst Eng Electron 31(01):109–117

    Article  Google Scholar 

  3. Gyu ML, Eun HC, Bong IC, Byeong HR, Dong KR, Gyudong P (2020) SDN-based development direction of C2 network system according to network technology development trend. J Korean Inst Commun Inf Sci 45:4. https://doi.org/10.7840/kics.2020.45.4.730

    Article  Google Scholar 

  4. Eisenberg DA, Alderson DL, Kitsak M, Ganin A, Linkov I (2018) Network foundation for command and control (c2) systems: literature review. IEEE Access 2018:6. https://doi.org/10.1109/access.2018.2873328

    Article  Google Scholar 

  5. Hu B, Li F (2020) Repair strategies of scale-free networks under multifold attack strategies. J Control Dec 32:86–89

    Google Scholar 

  6. Tian XG, Zhu YC, Luo K, Zhang CM (2013) Adaptive reconstruction model for command and control system under information age based on complex network theory. J Control Dec 35:91–96. https://doi.org/10.3969/j.issn.1001-506X.2013.01.15

    Article  Google Scholar 

  7. Wang Z, Li JH, Kang D (2020) Research on recovering of complex networks based on boundary nodes of giant connected component. J Syst Simul 32(12):2306–2316. https://doi.org/10.16182/j.issn1004731x.joss.20-fz0295

    Article  Google Scholar 

  8. Fu ZH, Sun L, Lin ZZ, Wen FS, Zhu BQ, Xu LZ (2016) Bi-level network reconfiguration optimization based on node importance evaluation matrix. Electr Power Autom Equip 36:37–42. https://doi.org/10.16081/j.issn.1006-6047.2016.05.006

    Article  Google Scholar 

  9. Chen XN, Hu JM, Chi BL, Cui Y (2021) Game and reconfiguration in complex battle network system. Acta Armamentarii 42(05):1111–1120. https://doi.org/10.3969/j.issn.1000-1093.2021.05.024

    Article  Google Scholar 

  10. Zhuo M, Liu LY, Zhou SJ, Yang P, Wan SM (2021) A new method for invulnerability analysis of spatial information networks. J Guangxi Normal Univ Natural Sci Ed 39:21–31. https://doi.org/10.16088/j.issn.1001-6600.2020082601

    Article  Google Scholar 

  11. Wang ZX, Jiang DL, Qi L, Chen X, Zhao YB (2020) Complex network invulnerability and node importance evaluation model based on redundancy. Compl Syst Compl Sci 17:78–85

    Google Scholar 

  12. Li SB, Huang JW, Liu JH, Huang TP, Chen HH (2020) Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor. Chaos Woodbury NY. https://doi.org/10.1063/1.5094448

    Article  MATH  Google Scholar 

  13. Oğuz F, Emrah Ö (2020) Link prediction based on node weighting in complex networks. Soft Comput 2020:1–16. https://doi.org/10.1007/s00500-020-05314-8

    Article  Google Scholar 

  14. Gao TR, Zhu XZ (2020) Link prediction based on hybrid influence of neighbors. Int J Modern Phys B 34:10. https://doi.org/10.1142/S0217979220500186

    Article  Google Scholar 

  15. Víctor M, Fernando B, Juan-Carlos C (2016) A survey of link prediction in complex networks. ACM Comput Surv (CSUR) 49:1–33. https://doi.org/10.1007/978-3-319-67582-4_28

    Article  Google Scholar 

  16. Zhou MY, Liao H, Xiong WM, Wu XY, Wei ZW (2017) Connection patterns inspire link prediction in complex networks. Complexity 2017:8581365. https://doi.org/10.1155/2017/8581365

    Article  MATH  Google Scholar 

  17. Wang MX, Lou XY, Cui BT (2021) A degree-related and link clustering coefficient approach for link prediction in complex networks. Eur Phys J B 94:33. https://doi.org/10.1140/epjb/s10051-020-00037-z

    Article  Google Scholar 

  18. He X, Zhao H, Cai W, Liu Z, Si SZ (2014) Earthquake networks based on space-time influence domain. Phys A: Stat Mech Appl 407:175–184. https://doi.org/10.1016/j.physa.2014.03.093

    Article  Google Scholar 

  19. Feng X, Zhao JC, Xu K (2012) Link prediction in complex networks: a clustering perspective. Eur Phys J B 85:1–9. https://doi.org/10.1140/epjb/e2011-20207-x

    Article  Google Scholar 

  20. Ma C, Chen HS, Lai YC, Zhang HF (2018) Statistical inference approach to structural Reconfiguration of complex networks from binary time series. Phys Rev E. https://doi.org/10.1103/PhysRevE.97.022301

    Article  Google Scholar 

  21. Chao M, Jiang XS, Wei XM (2020) A complex network reconstruction method based on multiple time series. J Circ Syst Comput 29:2050213. https://doi.org/10.1142/S0218126620502138

    Article  Google Scholar 

  22. Li JW, Shen ZS, Wang WX, Grebogi C, Lai YC (2017) Universal data-based method for reconstructing complex networks with binary-state dynamics. Phys Rev E. https://doi.org/10.1103/PhysRevE.95.032303

    Article  Google Scholar 

  23. Huang ZH, Dai PL, Jia SS, Yu ZF (2020) Network structure reconstruction with symmetry constraint. Chaos Solit Fract Interdiscipl J Nonlinear Sci Nonequilibr Compl Phenom. https://doi.org/10.1016/j.chaos.2020.110287

    Article  Google Scholar 

  24. Pan CS, Li J, Cai RY, Yang L (2020) Network reconfiguration technology based on improved bee colony algorithm. J Chin Comput Syst 41(01):144–148. https://doi.org/10.3969/j.i.ssn.1000-1220.2020.01.028

    Article  Google Scholar 

  25. Wu K, Hao XX, Liu J, Liu PH, Shen F (2020) Online reconstruction of complex networks from streaming data. IEEE Trans Cybern 2020:33147156. https://doi.org/10.1109/TCYB.2020.3027642

    Article  Google Scholar 

  26. Wang XH, Liu XY, Jian SC, Peng XG, Yuan HL (2021) A distribution network reconfiguration method based on comprehensive analysis of operation scenarios in the long-term time period. Energy Rep 7(S1):369–379. https://doi.org/10.1016/J.EGYR.2021.01.057

    Article  Google Scholar 

  27. Merzoug Y, Abdelkrim B, Larbi B (2020) Distribution network reconfiguration for loss reduction using PSO method. Int J Electr Comput Eng 10(5):5009–5015. https://doi.org/10.11591/IJECE.V10I5.PP5009-5015

    Article  Google Scholar 

  28. Kunt AA, Berberler ZN (2020) Efficient identification of node importance based on agglomeration in cycle-related networks. Int J Found Comput Sci 31:7. https://doi.org/10.1142/S0129054120500379

    Article  MathSciNet  MATH  Google Scholar 

  29. Liu YS, Wang JJ, He HT, Huang GY, Shi WB (2021) Identifying important nodes affecting network security in complex networks. Int J Distrib Sensor Netw 17:2. https://doi.org/10.1177/1550147721999285

    Article  Google Scholar 

  30. Jiang JL, Fang H, Li SQ, Li WM (2022) Identifying important nodes for temporal networks based on the ASAM model. Phys A Stat Mech Appl 2022:586. https://doi.org/10.1016/j.physa.2021.126455

    Article  Google Scholar 

  31. Wang YM, Chen S, Pan CS, Chen B (2018) Measure of invulnerability for command and control network based on mission link. Inf Sci 426:148–159. https://doi.org/10.1016/j.ins.2017.10.035

    Article  Google Scholar 

  32. Yang Q, Ding L (2020) Research on Internet Robustness Based on Node Intentional Attacks. Comput Modern 7:38–49. https://doi.org/10.3969/j.issn.1006-2475.2020.07.008

    Article  Google Scholar 

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Funding

This work was supported by Innovative Research Group Project of the National Natural Science Foundation of China (Grant no. 61471080), Quipment development department research foundation of China (Grant no. 61400010303), surface project for Natural Science foundation in Guangdong Province of China (Grant no. 2019A1515011164) and Science and Technology Plan project in Zhanjiang (Grant no. 2018A06001).

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Correspondence to Bo Chen.

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Gao, X., Chen, B., Jiang, P. et al. Hierarchy-entropy based method for command and control networks reconfiguration. J Supercomput 78, 15229–15249 (2022). https://doi.org/10.1007/s11227-022-04445-z

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