The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be diff...The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.展开更多
Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homo...Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.展开更多
A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associat...A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.展开更多
The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteris...The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteristic in the P2P system,TLI can identify whether or not the network data flow belongs to the P2P system.The DPI method adopts protocol analysis technology and reverting technology.It picks up data from the P2P application layer and analyzes the characteristics of the payload to judge if the network traffic belongs to P2P applications.Due to its accuracy,robustness and classifying ability,DPI is the main method used to identify P2P traffic.Adopting the advantages of TLI and DPI,a precise and efficient technology for P2P network traffic identification can be designed.展开更多
针对目前RFID(Radio Frequency Identification,射频识别技术)系统安全分析中忽略攻击事件对系统安全状态动态影响的问题,为了有效实现RFID系统的安全风险评估,文章提出了一种基于贝叶斯攻击图的RFID系统安全评估模型。该模型首先通过对...针对目前RFID(Radio Frequency Identification,射频识别技术)系统安全分析中忽略攻击事件对系统安全状态动态影响的问题,为了有效实现RFID系统的安全风险评估,文章提出了一种基于贝叶斯攻击图的RFID系统安全评估模型。该模型首先通过对RFID系统结构、所用协议进行分析确定系统的脆弱性漏洞及其依赖关系,建立攻击图。针对攻击图模型只能进行定性分析的问题,构建出相应的攻击图模型结构后可以结合贝叶斯理论对其进行量化。依据漏洞的利用难易度和影响程度建立RFID漏洞量化评价指标,计算出对应的原子攻击概率,然后以条件转移概率的形式将攻击节点与RFID系统的安全属性节点联系在一起,不仅能推断攻击者能够成功到达各个属性节点的风险概率,而且能够依据攻击者的不同行为动态展示系统风险状况的变化,实现评估不同状态下目标RFID系统的整体风险状况。实验表明,所提模型可以有效地计算出RFID系统整体的风险概率,为后续实施对应的安全策略提供理论依据。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61961019)the Youth Key Project of the Natural Science Foundation of Jiangxi Province of China(Grant No.20202ACBL212003).
文摘The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.
基金supported by the National Natural Science Foundation of China(Grant No.61231010)the Fundamental Research Funds for the Central Universities,China(Grant No.HUST No.2012QN076)
文摘Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.
文摘A new method of fault domain identification is proposed based on K-means clustering analysis theories using the wide-area information of power grid. In the method, the node Intelligent Electronic Device (IED) associated domain is defined, and the relationship of positive sequence current fault component for the association domain boundaries is sought, then the conception of positive sequence fault component differential current for node IED association domains is introduced. The information of the positive sequence fault component differential current gathered by node IEDs is selected as the object of K-means clustering. The node IEDs of fault associated domains can be classified into one category, and the node IEDs of non-fault associated domains are classified into another category. With the fault area minimum principle, the group of node IEDs about fault associated domains can be obtained. The overlap of fault associated domains for different nodes is the fault area. A large number of simulations show that the algorithm proposed can identify fault domains with high accuracy and no influence by the operating mode of the system and topological changes.
基金This work was funded by the National Natural Science Foundation of China under Grant60473090.
文摘The Peer-to-Peer(P2P)network traffic identification technology includes Transport Layer Identification(TLI)and Deep Packet Inspection(DPI)methods.By analyzing packets of the transport layer and the traffic characteristic in the P2P system,TLI can identify whether or not the network data flow belongs to the P2P system.The DPI method adopts protocol analysis technology and reverting technology.It picks up data from the P2P application layer and analyzes the characteristics of the payload to judge if the network traffic belongs to P2P applications.Due to its accuracy,robustness and classifying ability,DPI is the main method used to identify P2P traffic.Adopting the advantages of TLI and DPI,a precise and efficient technology for P2P network traffic identification can be designed.
文摘针对目前RFID(Radio Frequency Identification,射频识别技术)系统安全分析中忽略攻击事件对系统安全状态动态影响的问题,为了有效实现RFID系统的安全风险评估,文章提出了一种基于贝叶斯攻击图的RFID系统安全评估模型。该模型首先通过对RFID系统结构、所用协议进行分析确定系统的脆弱性漏洞及其依赖关系,建立攻击图。针对攻击图模型只能进行定性分析的问题,构建出相应的攻击图模型结构后可以结合贝叶斯理论对其进行量化。依据漏洞的利用难易度和影响程度建立RFID漏洞量化评价指标,计算出对应的原子攻击概率,然后以条件转移概率的形式将攻击节点与RFID系统的安全属性节点联系在一起,不仅能推断攻击者能够成功到达各个属性节点的风险概率,而且能够依据攻击者的不同行为动态展示系统风险状况的变化,实现评估不同状态下目标RFID系统的整体风险状况。实验表明,所提模型可以有效地计算出RFID系统整体的风险概率,为后续实施对应的安全策略提供理论依据。
