摘要
为探究轨道交通网络多个关联站点受到蓄意攻击后网络的脆弱性以及不同攻击下影响的差异,量测分析多站点之间的关联性,给出关联站点协同攻击下轨道交通网络脆弱性量化方法,提出以脆弱性最大化为目标的攻击模型,通过免疫算法求解模型,得到最优攻击策略,并以上海市地铁网络为例,研究多站点协同攻击下轨道交通网络脆弱性。结果表明:多站点蓄意攻击下轨道交通网络脆弱性不仅取决于每个站点的重要程度,还取决于不同站点之间的关联性,尤其是空间位置关联性和乘客出行关联性。位于不同线路且客流量较大的多个换乘站点受到协同攻击下轨道交通网络脆弱性最高,占网络站点总数3.56%的多个站点同时受到攻击后,轨道交通网络性能损失最高可达63.61%。
In order to explore the vulnerability of the rail transit network after multiple associated stations were deliberately attacked and the differences in the impact of different attacks,the correlation between multiple stationswas measured and analyzed,and the vulnerability quantification method of the rail transit network under the coordinated attack of the associated stations was given.An attack model aiming at maximizing vulnerability was proposed,and the model was solved by immune algorithm to obtain the optimal attack strategy.Taking Shanghai metro network as an example,the vulnerability of rail transit networks under multi stations coordinated attack was studied.The results show that the vulnerability of rail transit networks under multi stations deliberate attack depends not only on the importance of each station,but also on the correlation between different stations,especially the spatial location correlation and passenger travel correlation.Multiple transfer stations located on different lines and with large passenger flow have the highest vulnerability to coordinated attacks.After multiple stations accounting for 3.56%of the total number of network stations are attacked at the same time,the rail transit network performance loss can reach up to 63.61%.
作者
崔欣
路庆昌
张磊
徐标
秦汉
刘鹏
CUI Xin;LU Qingchang;ZHANG Lei;XU Biao;QIN Han;LIU Peng(School of Electronics and Control Engineering,Chang'an University,Xi'an Shaanxi 710064,China;School of Highway and Railway Engineering,Shaanxi College of Communications Technology,Xi'an Shaanxi 710018,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2023年第3期167-173,共7页
China Safety Science Journal
基金
国家自然科学基金面上项目资助(7197102)
陕西省自然科学基础研究计划项目(2021JC-28)。
关键词
关联站点
协同攻击
轨道交通网络
脆弱性
关联性
免疫算法
associated stations
coordinated attack
rail transit network
vulnerability
correlation
immune algorithm
