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关键基础设施网络体系易损性定量评估
引用本文:赵旭东,陈志龙,龚华栋,郭东军.关键基础设施网络体系易损性定量评估[J].解放军理工大学学报,2016(3):241-245.
作者姓名:赵旭东  陈志龙  龚华栋  郭东军
作者单位:解放军理工大学 爆炸冲击防灾减灾国家重点实验室,江苏 南京 210007,解放军理工大学 爆炸冲击防灾减灾国家重点实验室,江苏 南京 210007,解放军理工大学 爆炸冲击防灾减灾国家重点实验室,江苏 南京 210007,解放军理工大学 爆炸冲击防灾减灾国家重点实验室,江苏 南京 210007
基金项目:国家自然科学基金资助项目(51021001)
摘    要:为了保护关键基础设施网络在战时的系统功能,基于复杂网络理论建立了基础设施网络体系易损性评估框架。在该框架中,以控制时间内网络终端节点的各类需求满足程度作为基础设施网络效能的评价指标,融合规划布局、连通通道冗余备份、抢修抢建等因素,设计体系易损性评估公式,并以某交通运输体系为例进行案例研究。结果表明,在控制时间内,该网络体系效能在4种随机攻击模式中的最大损失率约为17%,最小损失率约为7%。在各种袭击场景下随机增加网络连通通道,体系性能损失率没有明显变化,因而以往随机增加基础设施网络通道等手段,并不能够降低体系易损性。

关 键 词:关键基础设施    复杂网络  易损性  定量评估
收稿时间:2015/5/14 0:00:00

Quantitative assessment of vulnerability for critical infrastructure network
ZHAO Xudong,CHEN Zhilong,GONG Huadong and GUO Dongjun.Quantitative assessment of vulnerability for critical infrastructure network[J].Journal of PLA University of Science and Technology(Natural Science Edition),2016(3):241-245.
Authors:ZHAO Xudong  CHEN Zhilong  GONG Huadong and GUO Dongjun
Institution:State Key Laboratory for Disaster Prevention and Mitigation of Explosion and Impact, PLA Univ. of Sci. & Tech., Nanjing 210007, China,State Key Laboratory for Disaster Prevention and Mitigation of Explosion and Impact, PLA Univ. of Sci. & Tech., Nanjing 210007, China,State Key Laboratory for Disaster Prevention and Mitigation of Explosion and Impact, PLA Univ. of Sci. & Tech., Nanjing 210007, China and State Key Laboratory for Disaster Prevention and Mitigation of Explosion and Impact, PLA Univ. of Sci. & Tech., Nanjing 210007, China
Abstract:In order to protect the system function of critical infrastructure network in the wartime, a vulnerability assessment framework was established based on the complex network theory. By measuring the demands satisfaction degree of network terminal node, a metric for assessing the vulnerability was constructed which includes some significant factors such as the infrastructures network planning, the path redundancy and emergency recovery actions. A typical transportation network was taken as a case to illustrate the assessment framework. The study results show that under four randomly generated attack scenarios, the maximum loss ratio of demands satisfaction degree approaches 17% within the control time, while the minimum loss ratio of demands satisfaction degree is 7%. When a network link is randomly added, the maximum loss ratio of demands satisfaction degree has no significant change. It means that the random addition of network link can''t guarantee the reduction of the vulnerability of the infrastructure network.
Keywords:
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