Hierarchy-entropy based method for command and control networks reconfiguration
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) ne...
Ausführliche Beschreibung
Autor*in: |
Gao, Xiue [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 78(2022), 13 vom: 15. Apr., Seite 15229-15249 |
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Übergeordnetes Werk: |
volume:78 ; year:2022 ; number:13 ; day:15 ; month:04 ; pages:15229-15249 |
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DOI / URN: |
10.1007/s11227-022-04445-z |
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Katalog-ID: |
SPR047794585 |
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520 | |a 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. | ||
650 | 4 | |a Command and control |7 (dpeaa)DE-He213 | |
650 | 4 | |a Network reconfiguration |7 (dpeaa)DE-He213 | |
650 | 4 | |a Hierarchy-entropy |7 (dpeaa)DE-He213 | |
650 | 4 | |a Complex network |7 (dpeaa)DE-He213 | |
700 | 1 | |a Chen, Bo |0 (orcid)0000-0002-9689-1252 |4 aut | |
700 | 1 | |a Jiang, Panling |4 aut | |
700 | 1 | |a Xiang, Zhengtao |4 aut | |
700 | 1 | |a Chen, Yufeng |4 aut | |
700 | 1 | |a Wang, Yunming |4 aut | |
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10.1007/s11227-022-04445-z doi (DE-627)SPR047794585 (SPR)s11227-022-04445-z-e DE-627 ger DE-627 rakwb eng Gao, Xiue verfasserin aut Hierarchy-entropy based method for command and control networks reconfiguration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 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. Command and control (dpeaa)DE-He213 Network reconfiguration (dpeaa)DE-He213 Hierarchy-entropy (dpeaa)DE-He213 Complex network (dpeaa)DE-He213 Chen, Bo (orcid)0000-0002-9689-1252 aut Jiang, Panling aut Xiang, Zhengtao aut Chen, Yufeng aut Wang, Yunming aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 13 vom: 15. Apr., Seite 15229-15249 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:13 day:15 month:04 pages:15229-15249 https://dx.doi.org/10.1007/s11227-022-04445-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 13 15 04 15229-15249 |
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10.1007/s11227-022-04445-z doi (DE-627)SPR047794585 (SPR)s11227-022-04445-z-e DE-627 ger DE-627 rakwb eng Gao, Xiue verfasserin aut Hierarchy-entropy based method for command and control networks reconfiguration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 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. Command and control (dpeaa)DE-He213 Network reconfiguration (dpeaa)DE-He213 Hierarchy-entropy (dpeaa)DE-He213 Complex network (dpeaa)DE-He213 Chen, Bo (orcid)0000-0002-9689-1252 aut Jiang, Panling aut Xiang, Zhengtao aut Chen, Yufeng aut Wang, Yunming aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 13 vom: 15. Apr., Seite 15229-15249 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:13 day:15 month:04 pages:15229-15249 https://dx.doi.org/10.1007/s11227-022-04445-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 13 15 04 15229-15249 |
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10.1007/s11227-022-04445-z doi (DE-627)SPR047794585 (SPR)s11227-022-04445-z-e DE-627 ger DE-627 rakwb eng Gao, Xiue verfasserin aut Hierarchy-entropy based method for command and control networks reconfiguration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 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. Command and control (dpeaa)DE-He213 Network reconfiguration (dpeaa)DE-He213 Hierarchy-entropy (dpeaa)DE-He213 Complex network (dpeaa)DE-He213 Chen, Bo (orcid)0000-0002-9689-1252 aut Jiang, Panling aut Xiang, Zhengtao aut Chen, Yufeng aut Wang, Yunming aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 13 vom: 15. Apr., Seite 15229-15249 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:13 day:15 month:04 pages:15229-15249 https://dx.doi.org/10.1007/s11227-022-04445-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 13 15 04 15229-15249 |
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10.1007/s11227-022-04445-z doi (DE-627)SPR047794585 (SPR)s11227-022-04445-z-e DE-627 ger DE-627 rakwb eng Gao, Xiue verfasserin aut Hierarchy-entropy based method for command and control networks reconfiguration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 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. Command and control (dpeaa)DE-He213 Network reconfiguration (dpeaa)DE-He213 Hierarchy-entropy (dpeaa)DE-He213 Complex network (dpeaa)DE-He213 Chen, Bo (orcid)0000-0002-9689-1252 aut Jiang, Panling aut Xiang, Zhengtao aut Chen, Yufeng aut Wang, Yunming aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 13 vom: 15. Apr., Seite 15229-15249 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:13 day:15 month:04 pages:15229-15249 https://dx.doi.org/10.1007/s11227-022-04445-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 13 15 04 15229-15249 |
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10.1007/s11227-022-04445-z doi (DE-627)SPR047794585 (SPR)s11227-022-04445-z-e DE-627 ger DE-627 rakwb eng Gao, Xiue verfasserin aut Hierarchy-entropy based method for command and control networks reconfiguration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 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. Command and control (dpeaa)DE-He213 Network reconfiguration (dpeaa)DE-He213 Hierarchy-entropy (dpeaa)DE-He213 Complex network (dpeaa)DE-He213 Chen, Bo (orcid)0000-0002-9689-1252 aut Jiang, Panling aut Xiang, Zhengtao aut Chen, Yufeng aut Wang, Yunming aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2022), 13 vom: 15. Apr., Seite 15229-15249 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2022 number:13 day:15 month:04 pages:15229-15249 https://dx.doi.org/10.1007/s11227-022-04445-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 13 15 04 15229-15249 |
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hierarchy-entropy based method for command and control networks reconfiguration |
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Hierarchy-entropy based method for command and control networks reconfiguration |
abstract |
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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
abstractGer |
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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
abstract_unstemmed |
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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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title_short |
Hierarchy-entropy based method for command and control networks reconfiguration |
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https://dx.doi.org/10.1007/s11227-022-04445-z |
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Chen, Bo Jiang, Panling Xiang, Zhengtao Chen, Yufeng Wang, Yunming |
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Chen, Bo Jiang, Panling Xiang, Zhengtao Chen, Yufeng Wang, Yunming |
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2024-07-03T15:01:28.026Z |
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score |
7.401825 |