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Maximizing Network Resilience against Malicious Attacks

delete2019-02-19
delete20
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OA
AI
李稳国 封面图
李稳国 (Wenguo Li)
李勇 封面图
李勇 (Yong Li)
Y
Yi Tan *
Y
Yijia Cao
陈春 封面图
陈春 (Chun Chen)
蔡晔 封面图
蔡晔 (Ye Cai)
K
Kwang Y. Lee
M
Michael Pecht
DOI:10.1038/s41598-019-38781-7delete
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摘要

摘要

En 中文
The threat of a malicious attack is one of the major security problems in complex networks. Resilience is the system-level self-adjusting ability of a complex network to retain its basic functionality and recover rapidly from major disruptions. Despite numerous heuristic enhancement methods, there is a research gap in maximizing network resilience: current heuristic methods are designed to immunize vital nodes or modify a network to a specific onion-like structure and cannot maximize resilience theoretically via network structure. Here we map complex networks onto a physical elastic system to introduce indices of network resilience, and propose a unified theoretical framework and general approach, which can address the optimal problem of network resilience by slightly modifying network structures (i.e., by adding a set of structural edges). We demonstrate the high efficiency of this approach on three realistic networks as well as two artificial random networks. Case studies show that the proposed approach can maximize the resilience of complex networks while maintaining their topological functionality. This approach helps to unveil hitherto hidden functions of some inconspicuous components, which in turn, can be used to guide the design of resilient systems, offer an effective and efficient approach for mitigating malicious attacks, and furnish self-healing to reconstruct failed infrastructure systems.
Keyword:
COMPLEX NETWORKS
IDENTIFICATION
ROBUSTNESS
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Scientific Reports 封面图
Scientific Reports
IF:
3.9
论文数:
28.1W
被引数:
83.5W

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Baylor University
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hunan university
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论文数: 3.3W
被引数: 70
University System of Maryland 封面图
University System of Maryland
学者数:
6.5W
论文数: 5.6W
被引数: 113
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