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Disintegration of Operational Capability of Heterogeneous Combat Networks Under Incomplete Information
The combat system-of-systems (CSoSs) in high-tech information warfare, composed of different types of combat systems connected through multiple interconnections, can be abstracted as a type of complex heterogeneous network. Research on the disintegration of the operational capability of a heterogene...
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Published in: | IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2020-12, Vol.50 (12), p.5172-5179 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The combat system-of-systems (CSoSs) in high-tech information warfare, composed of different types of combat systems connected through multiple interconnections, can be abstracted as a type of complex heterogeneous network. Research on the disintegration of the operational capability of a heterogeneous combat network (HCN) is of significant military value for optimizing the planning of operation process and improving network survivability in a complex electromagnetic battlefield. Accordingly, this paper presents an integrated framework called HCN operational capability disintegration based on link prediction (OCDLP) for modeling and solving the disintegration problem regarding the operational capability of an HCN. More specifically, a heterogeneous network model of a combat network under incomplete information is first established, considering different types of functional entities and information flows. Based on this model, a link prediction model is then applied to recover the original combat network structure while taking advantage of the observed information. Next, a disintegration evaluation metric of the operational capability of an HCN is elicited to evaluate the attack efficiency after a link prediction. Last, the reliability and effectiveness of the OCDLP are illustrated through a case study. The results provide useful insight into the operation guidance and design of a more resilient CSoSs. |
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ISSN: | 2168-2216 2168-2232 |
DOI: | 10.1109/TSMC.2018.2867532 |