Security Protection Method for Key Nodes of Intelligent Traffic Network Based on Time Series Data and Probabilistic Models
To solve the problem of poor protection effect of key nodes in intelligent traffic network due to the lack of dispersion degree analysis,a security protection method for key nodes in intelligent traffic network based on time series data and probabi-listic models is proposed.By combining time series data of traffic flow and node connectivity probabilistic models,it selects key feature parameters of the traffic network,clarifies the degree of internal dispersion and connectivity performance of the net-work.Using key feature parameters as centrality indicators,the importance of nodes is solved to achieve identification of key nodes.A traffic flow balance model is established for key nodes,the traffic load status of nodes are balanced,and node security protection is achieved.The experimental results show that after using the proposed method to provide security protection for key nodes in the traffic network,the traffic flow load ratio of the nodes is significantly reduced,and it has a relatively ideal se-curity protection effect.
time series dataprobabilistic modeltraffic networkkey node
万方数据
维普
