Research On Whitelist Technology Of Industrial Firewall Based On ACO-PSO-SVM

Informationization of the big data of The Times,the development of the industrial field is more and more dependent on the Internet,followed by the Internet end of the industrial control system invasion is also increasing,the security of the Internet has seriously affected the development of the current industrial field.Industrial firewall is widely used in the field of industrial control network security,its security protection principle of industrial control data analysis after extracting data characteristics,to create a whitelist database,in order to intercept abnormal data attacks.This paper focuses on analyzing and judging the problems of industrial control system.To judge the industrial firewall in the organization of industrial control system management positioning.Also describes the current industrial control system is the most widely used Modbus TCP protocol,analyze the agreement itself vulnerable to attack specific reasons,and according to the characteristics of the industrial control system chooses white list strategy of the firewall,industrial automation to design the self-learning technology of the white list and anomaly detection model of support vector machine(SVM)fusion,to identify communication data.Secondly,in order to improve the accuracy of the detection model,the penalty factor and kernel function,which affect the classification accuracy of support vector machine,are optimized.In this paper,an intelligent algorithm is introduced to search for the optimal parameters.This paper analyzes the principle and characteristics of particle swarm optimization(PSO),and a detection model that uses the hybrid algorithm of particle swarm optimization and ant colony algorithm to optimize the parameters of support vector machine is designed.The obtained data are grouped and numbered.Particle swarm optimization algorithm can be used to match the fastest solution in the early stage,and ant colony algorithm is the most suitable in the late stage.The ant colony particle swarm hybrid algorithm can find the optimal solution based on data and support vector machine parameters such as penalty factor.The simulation results show that the ant colony particle swarm hybrid algorithm studied in this paper can effectively improve the accuracy of industrial control data classification.Finally,the simulated intelligent three-dimensional parking garage control system is built.After completing various parameter Settings,the data packet capture software is used to capture the operation data of the industrial control system and create a whitelist rule library.Respectively using the standard particle swarm optimization(PSO)algorithm,the grid search method,ant colony mixed particle swarm algorithm,the implementation of the simulation test,based on the experimental results are analyzed: using a hybrid algorithm of normal data and abnormal data classification accuracy compared to the single particle swarm optimization(PSO)algorithm and grid method has a certain extent,test analysis and design in the role and effect of data classification algorithm.