张震^1,,,
刘迪洋¹,
张进²,
谢记超¹
1.战略支援部队信息工程大学 郑州 450000
2.网络通信与安全紫金山实验室 南京 210000
基金项目:国家自然科学基金(61802429, 61872382, 61521003)，国家重点研发计划(2017YFB0803201, 2017YFB0803204)

详细信息

作者简介:张震：男，1986年生，讲师，博士，硕士生导师，主要研究方向为主动防御、新型网络体系架构
刘迪洋：男，1995年生，硕士生，研究方向为复杂网络鲁棒性优化、主动防御
张进：男，1979年生，工程师，博士，主要研究方向为宽带信息网络、网络安全
谢记超：男，1993年生，硕士，助理研究员，研究方向为新型网络体系结构、网络安全

通讯作者:张震　zhangzhen2096@163.com

中图分类号:TN919.2

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被引次数:0

出版历程

收稿日期:2020-03-20
修回日期:2020-09-02
网络出版日期:2020-09-17
刊出日期:2021-05-18

Research on the Robustness of Complex Networks Based on Dynamic Control of Node Redundancy Capacity

Zhen ZHANG^1,,,
Diyang LIU¹,
Jin ZHANG²,
Jichao XIE¹
1. PLA Strategic Support Force Information Engineering University, Zhengzhou 450000, China
2. Network Communication and Security Purple Mountain Laboratory, Nanjing 210000, China
Funds:The National Natural Science Foundation of China(61802429, 61872382, 61521003), The National Key Research and Development Plan(2017YFB0803201, 2017YFB0803204)


摘要
摘要:针对传统级联失效模型中冗余参数固定不变的问题，该文综合考虑节点受攻击程度不同和失效过程中网络拓扑的动态变化，建立了基于节点冗余容量动态控制(DRC)的级联失效模型。通过定义网络相变临界因子$\theta $衡量节点失效引发级联失效的概率，分析了网络鲁棒性与$\theta $之间的相关性，并结合度分布函数详细推导了$\theta $的解析表达式，基于解析表达式提出了两种网络鲁棒性提升策略。仿真结果表明，在模型网络和真实网络中，根据被攻击节点度的不同，通过调整节点初始负载参数$\tau $可以有效提高目标网络的鲁棒性；DRC模型下级联失效传播范围较Motter-Lai(ML)模型显著减小。
关键词:复杂网络/
级联失效/
网络鲁棒性/
节点冗余容量
Abstract:In View of the problem of fixed redundancy parameters in the traditional cascade failure model, this paper comprehensively considers the different attack levels of nodes and the dynamic changes of the network topology during the failure process, and establishes a cascading failure model based on Dynamic control of node Redundancy Capacity (DRC). By defining the critical factor $\theta $ of the phase transition of the network to measure the probability of node failure leading to cascading failure, the correlation between network robustness and $\theta $ is analyzed, and the analytic expression of $\theta $ is derived in detail by combining degree distribution function, Based on analytic expressions, two network robustness enhancement strategies are proposed. The simulation results show that in model network and real network, the robustness of target network can be effectively improved by adjusting the initial load parameter $\tau $ of nodes according to the difference of degree of nodes under attack. The failure propagation range of DRC model is significantly reduced compared with Motter-Lai (ML) model.
Key words:Complex networks/
Cascade failure/
Robustness/
Node redundancy capacity



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