Industrial control network abnormal detection based on optimized random forest model
ZONG Xuejun
WANG Runpeng
HE Kan
LIAN Lian
Abstract：Aiming at the lower efficiency and accuracy of the existing Modbus TCP protocol for abnormal detection,a random forest abnormal detection model based on hybrid whale optimization algorithm was proposed.In this model,Cauchy mutation and adaptive dynamic inertia weight were combined.On the one hand,Cauchy mutation operator was used to increase population diversity and avoid the algorithm falling into local optimum.On the other hand,the adaptive dynamic inertia weight factor was used to improve the global search ability of the population,in order to increase the convergence speed of algorithm.Simulation results show that the as-proposed model has higher accuracy and stronger adaptability than other classification algorithms,indicating that the model also has higher detection accuracy in practical application.
Keywords：industrial control networkabnormal detectionindustrial protocolwhale optimization algorithmrandom forestchaotic mappingCauchy mutationself adaptive weight
Publication Date：2024-03-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 197-205 )