Research On Performance Evaluation Of Information Security Risk Management In Smart City Based On BP Neural Network

In this different age,urban construction has integrated big data,artificial intelligence,Internet of Things and other information technologies applied in reality,forming the current smart city.Smart city construction is committed to solving various problems in urban development,greatly improving the convenience and practicality of the city,and also improving the quality of life of residents,so that the people have more sense of gain.After vigorous development in recent years,the construction of smart cities in China has gradually transformed from the initial stage of crossing the river by feeling the stones to the stage of rational exploration.While promoting the development of smart cities,information technology also brings new information security risks and challenges.Facing the increasingly prominent information security risk problem of smart cities,it is imperative to accelerate the implementation of risk management and performance evaluation.Therefore,in view of the status quo of information security risk management in smart cities,this paper aims to evaluate the performance of information security risk management in smart cities.To begin with,we should construct the performance evaluation index system of information security risk management in smart city.Then,grey correlation analysis method is used to calculate and determine the performance value of information security risk management of each smart city,and its performance value is taken as the expected output.Furthermore,the GRA-BNPP model is used to test and train the sample data with MATLAB software,and it is found that there is little deviation between the actual results and the expected results.Lastly,according to the evaluation results,some problems in information security risk management are found,and then targeted suggestions and countermeasures are put forward to provide reference for relevant departments,improve the level of information security risk management,and ensure the sustainable development of smart cities.Firstly,this paper based on the smart city information security risk management perspective,combined with the actual development of China's smart city,objectively described the research background,and from the theoretical and practical significance of two aspects.Come to the conclusion that the research results of smart city at domestic and abroad,to understand the main directions in which research is needed.A combination of literature analysis,Delphi method,statistical analysis method,experimental research method,comparative analysis method and other methods were used to investigate the research and determine the research framework of this paper.Secondly,by referring to relevant literature,this paper expounds the information security and risk management of smart city,common performance evaluation methods and BP neural network.Then,following the principles of system completeness and scientificity,we construct the performance evaluation index system of information security risk management of smart cities through primary selection and revision,and finally analyze each index to interpret the rationality and validity of each index selection.Once again,the basis of model selection,as well as the ideas and processes of gray correlation analysis method and BP neural network are introduced,and the GRA-BPNN performance evaluation model of information security risk management in smart cities is constructed.Finally,selecting 25 major cities as research case in our country and putting the relevant data of these cities into MATLAB,and the model is trained,tested and simulated by using MATLAB software.The final values obtained by TOPSIS method and gray correlation analysis method are judged against the final values obtained by GRA-BPNN model to prove the advanced and universal applicability of the model constructed in this paper.We propose feasible countermeasures for the problems of information security risk management in smart cities.To looking forward to better serve the construction of smart cities.