Network Security Situation Element Extraction And Assessment For Video Private Network

The video private network is a video surveillance network established by the public security department,and plays an important role in security and safety management,case investigation,and convenience services.Video private network and related video information resources are important means of maintaining public safety and have become an important information infrastructure.However,due to the lack of overall security planning and insufficient security investment in the initial construction of the video private network,security risks have become increasingly prominent,and it is easy to become the target of criminals.Once a major cyber security event occurs,it will threaten national public safety.The network security situation element extraction and evaluation technology can timely monitor and overall master the security status of the video private network.Therefore,this article conducts in-depth research on the extraction and evaluation methods of the security situation elements of the video private network.1.This article first analyzes and studies the current network security risks existing in the video private network and the traditional network security index system.Secondly,according to the characteristics of the video private network and the combination of traditional basic evaluation indicators,a multi-level and multi-dimensional video private network security situation index system was constructed,and the underlying index quantification formula was proposed,which provided the video private network security situation element extraction and evaluation basis.2.In order to solve the problem of low accuracy of network security situation extraction and slow detection speed,a network security situation element extraction method based on LDA(Linear Discriminant Analysis)-QGA(Quantum Genetic Algorithm)-XGBoost is proposed.Firstly,LDA is used to reduce the dimension of the original situation element information set,remove redundant situation elements and noise,and extract the optimal situation element subset at the same time;secondly,QGA is used to optimize the initial parameters of xgboost to solve the problem that the accuracy of the extraction model fluctuates greatly;finally,xgboost model is constructed by using the optimized parameters to extract the situation elements.In order to verify the effectiveness of the algorithm,the simulation data set is used to verify the proposed algorithm.3.Aiming at the problems of difficult configuration of Hidden Markov Model(HMM)parameters and low evaluation accuracy,this paper proposes an improved situation assessment method of Hidden Markov Model.Combining a crowd search algorithm with a strong global random search capability and a traditional parameter optimization algorithm(Baum-Welch algorithm,BW)to solve the problem that the BW algorithm is easy to fall into a local optimum.Then,it combines the analytic hierarchy process and adopts the local first and then the whole method to comprehensively consider the security risks of the video private network,and uses quantitative formula analysis to complete the final network security situation assessment and obtain the security status of the video private network within a specific time period,which is convenient for managers to make effective decisions in a timely manner.At the same time,the algorithm is verified by experiments.