摘要
传统复杂网络领域对网络防御问题的研究未考虑影响航空网络防御资源配置的主要因素,为此提出一种考虑机场位置、航线流量等因素的基于航空网络实际的防御策略,来降低网络风险。首先,利用节点脆弱性减少模型确定不同类型防御资源防御量与节点防御能力的函数关系;然后,对传统的重要度评价矩阵做出改进,考虑航线流量、机场位置等影响航空网络防御资源配置的因素对节点进行重要度排序,并分析节点与整个航空网络风险的关系;最后,在总防御资源量一定的情况下,利用模拟退火算法对各个节点配置的防御资源量进行求解,使得网络总风险最小。通过对随机生成网络与中国航空网络的实验发现,该优化策略在分配防御资源时,能够区分机场节点位置和流量的差异,相比于传统方法能够有效降低网络总风险。
In view of a deficiency that in the field of complex network,the traditional network defense strategies did not consider the main factors which will affect the aviation network defense resource optimization.Therefore,an aviation network strategy considering airport location and traffic flow based on the reality of aviation system is proposed to reduce the total risk of network.The relationship between different types of defense resource and node defense capability is determined based on the node vulnerability reduction model.By making some adjustments in importance evaluation matrix method,the traffic flow,airport location and some other factors are considered to rank the nodes,and the relationship between nodes and the aviation network total risk is analyzed.In the end,under the circumstance that the defense resource is limited,the problem is solved by simulated annealing algorithm to reduce the total risk of network.Through the tests on randomly generated network and Chinese aviation network,it shows that this optimization algorithm is capable of telling the difference between node location and traffic flow when allocating defense resources,and more efficient in lowering the risk of network compared with traditional methods.
作者
黄海清
甘旭升
蒋旭瑞
吴奇科
孙静娟
HUANG Haiqing;GAN Xusheng;JIANG Xurui;WU Qike;SUN Jingjuan(College of Science,Xijing University,Xi’an 710123,China;College of Air Traffic Control and Navigation,Air Force Engineering University,Xi’an 710051,China;Air Traffic Control Room,Troop No.93801 of PLA,Xi’an 712200,China)
出处
《航空工程进展》
CSCD
2020年第1期85-91,共7页
Advances in Aeronautical Science and Engineering
基金
西京学院校级教改项目(ZDKC201915)
关键词
航空网络
复杂网络
防御资源优化
模拟退火算法
aviation network
complex network
defense resource optimization
simulated annealing algorithm
