In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set f...In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.展开更多
The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect,which is easy to leads to the problem that the data is usurped.Starting from the ...The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect,which is easy to leads to the problem that the data is usurped.Starting from the application of the ATC(automatic train control)network,this paper focuses on the zero trust and zero trust access strategy and the tamper-proof method of information-sharing network data.Through the improvement of ATC’s zero trust physical layer authentication and network data distributed feature differentiation calculation,this paper reconstructs the personal privacy scope authentication structure and designs a tamper-proof method of ATC’s information sharing on the Internet.From the single management authority to the unified management of data units,the systematic algorithm improvement of shared network data tamper prevention method is realized,and RDTP(Reliable Data Transfer Protocol)is selected in the network data of information sharing resources to realize the effectiveness of tamper prevention of air traffic control data during transmission.The results show that this method can reasonably avoid the tampering of information sharing on the Internet,maintain the security factors of air traffic control information sharing on the Internet,and the Central Processing Unit(CPU)utilization rate is only 4.64%,which effectively increases the performance of air traffic control data comprehensive security protection system.展开更多
本文利用中尺度非静力WRF(Weather Research Forecast,Version 3.4)模式,针对1013号"鲇鱼"台风个例,通过对红外高光谱AIRS(Atmospheric Infrared Sounder)资料经过基于空间梯度信息的质量控制之后同化进入模式,来评估新的质...本文利用中尺度非静力WRF(Weather Research Forecast,Version 3.4)模式,针对1013号"鲇鱼"台风个例,通过对红外高光谱AIRS(Atmospheric Infrared Sounder)资料经过基于空间梯度信息的质量控制之后同化进入模式,来评估新的质量控制方法对同化效果的影响以及对台风数值模拟的改善情况。研究结果发现,如果仅仅基于WRFDA(WRF Data Assimilation system,Version 3.4)模式自带的质量控制系统,将会有部分梯度距平值明显较大超过阈值的资料被同化进入模式,而这些可能受到"污染"且误差较大的资料同化进入模式必将会导致同化结果有较大误差,影响分析结果的质量。而对AIRS资料经过基于空间梯度信息质量控制之后再同化进入模式,确实可将梯度距平值大于阈值的"坏点"剔除掉,从而使初始场的描述更加准确,台风路径的模拟精度在一定程度上得到提高。综上可知,基于空间梯度信息的质量控制方法整体上对改善同化效果有较好的正效应,对台风的数值模拟也起到一定的促进作用。展开更多
The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides incre...The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.展开更多
As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.D...As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.展开更多
探讨软件定义网络(Software Defined Network,SDN)在数据中心传输中的自适应流量管理。SDN架构的关键组成要素包括控制器、应用层和南向接口,通过控制平面和数据平面的分离,实现了网络的灵活性和可编程性。OpenFlow协议作为关键通信协议...探讨软件定义网络(Software Defined Network,SDN)在数据中心传输中的自适应流量管理。SDN架构的关键组成要素包括控制器、应用层和南向接口,通过控制平面和数据平面的分离,实现了网络的灵活性和可编程性。OpenFlow协议作为关键通信协议在SDN中得到广泛应用,为控制器提供了调整数据平面行为的标准化手段。在自适应流量管理方面,控制器通过实时监测和智能调整网络状态,识别瓶颈和拥塞点,并根据不同应用的性能需求进行精确的流量管理决策。基于流和基于应用的自适应管理算法使网络能够灵活适应不同流量负载,提高资源利用效率。流量监测工具如NetFlow和sFlow以及反馈机制在实现自适应流量管理中发挥关键作用,实时感知和调整网络状态,使SDN网络更加智能、适应性更强,并提供了优越的应用体验。未来的研究方向将关注SDN中自适应流量管理的创新策略,推动网络技术不断进步。展开更多
基金National Natural Science Foundation of China(U2133208,U20A20161)National Natural Science Foundation of China(No.62273244)Sichuan Science and Technology Program(No.2022YFG0180).
文摘In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.
基金This work was supported by National Natural Science Foundation of China(U2133208,U20A20161).
文摘The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect,which is easy to leads to the problem that the data is usurped.Starting from the application of the ATC(automatic train control)network,this paper focuses on the zero trust and zero trust access strategy and the tamper-proof method of information-sharing network data.Through the improvement of ATC’s zero trust physical layer authentication and network data distributed feature differentiation calculation,this paper reconstructs the personal privacy scope authentication structure and designs a tamper-proof method of ATC’s information sharing on the Internet.From the single management authority to the unified management of data units,the systematic algorithm improvement of shared network data tamper prevention method is realized,and RDTP(Reliable Data Transfer Protocol)is selected in the network data of information sharing resources to realize the effectiveness of tamper prevention of air traffic control data during transmission.The results show that this method can reasonably avoid the tampering of information sharing on the Internet,maintain the security factors of air traffic control information sharing on the Internet,and the Central Processing Unit(CPU)utilization rate is only 4.64%,which effectively increases the performance of air traffic control data comprehensive security protection system.
文摘本文利用中尺度非静力WRF(Weather Research Forecast,Version 3.4)模式,针对1013号"鲇鱼"台风个例,通过对红外高光谱AIRS(Atmospheric Infrared Sounder)资料经过基于空间梯度信息的质量控制之后同化进入模式,来评估新的质量控制方法对同化效果的影响以及对台风数值模拟的改善情况。研究结果发现,如果仅仅基于WRFDA(WRF Data Assimilation system,Version 3.4)模式自带的质量控制系统,将会有部分梯度距平值明显较大超过阈值的资料被同化进入模式,而这些可能受到"污染"且误差较大的资料同化进入模式必将会导致同化结果有较大误差,影响分析结果的质量。而对AIRS资料经过基于空间梯度信息质量控制之后再同化进入模式,确实可将梯度距平值大于阈值的"坏点"剔除掉,从而使初始场的描述更加准确,台风路径的模拟精度在一定程度上得到提高。综上可知,基于空间梯度信息的质量控制方法整体上对改善同化效果有较好的正效应,对台风的数值模拟也起到一定的促进作用。
基金supported by the Corporation Science and Technology Program of Global Energy Interconnection Group Ltd. (GEIGC-D-[2018]024)by the National Natural Science Foundation of China (61472042, 61772079)
文摘The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.
基金supported by the National Key Research and Development Program of China(No.2022YFB2602402)the National Natural Science Foundation of China(Nos.U2033215 and U2133210).
文摘As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.
文摘探讨软件定义网络(Software Defined Network,SDN)在数据中心传输中的自适应流量管理。SDN架构的关键组成要素包括控制器、应用层和南向接口,通过控制平面和数据平面的分离,实现了网络的灵活性和可编程性。OpenFlow协议作为关键通信协议在SDN中得到广泛应用,为控制器提供了调整数据平面行为的标准化手段。在自适应流量管理方面,控制器通过实时监测和智能调整网络状态,识别瓶颈和拥塞点,并根据不同应用的性能需求进行精确的流量管理决策。基于流和基于应用的自适应管理算法使网络能够灵活适应不同流量负载,提高资源利用效率。流量监测工具如NetFlow和sFlow以及反馈机制在实现自适应流量管理中发挥关键作用,实时感知和调整网络状态,使SDN网络更加智能、适应性更强,并提供了优越的应用体验。未来的研究方向将关注SDN中自适应流量管理的创新策略,推动网络技术不断进步。
