Research On Network Security Situation Assessment And Prediction Technology For E-Government Cloud

With the continuous development of China’s E-government cloud,network security construction under the E-government cloud environment has become one of the current hotspots.Traditional defense technology lacks the ability to actively identify security events in virtualized environments,marking traditional protection methods difficult to solve security problems in a cloud network environment.Based on the concept of E-government cloud,this paper focuses on the security cloud landing local,based on the use of network security situational awareness system to build active defense under the government cloud environment,intuitively quantify the network security situation,focusing on the current network security situation Techniques for conducting assessments,as well as techniques for predicting future cybersecurity postures.details as follows:(1)E-Government cloud network security situation assessment technology: Based on the experience of Hidden Markov Model(HMM)parameter expert,and affecting the objectivity of evaluation value,the adaptive clustering particle swarm optimization(ASPSO)optimization HMM evaluation technique is given..ASPSO algorithm introduces adaptive learning factor and inertia weight based on particle swarm optimization(PSO),and introduces the clustering behavior in artificial fish swarm algorithm.Therefore,it not only has the fast convergence characteristics of PSO algorithm.It also has a global search capability for clustering behavior.The optimized HMM is used for the network security situation assessment under the government cloud environment.The experimental results show that the evaluation results are in good agreement with the network attack situation,indicating that the method is objective and reasonable.(2)E-Government cloud network security situation prediction technology: Based on the problem of BP neural network training time and easy to fall into the local extreme value of the situational prediction value accuracy,the artificial fish swarm algorithm(AFSA)and adaptive genetic algorithm are respectively given.AGA)Optimized BP neural network(BPNN)situation prediction technology.The AFSA and AGA are used to optimize the connection weight and threshold parameters to obtain a better situation prediction model,and the optimized prediction model is used for network security situation prediction under the government cloud environment.The experimental results show that the convergence speed and prediction accuracy of AGA-BPNN and AFSA-BPNN are significantly improved.