Research on Dynamic Mining of Public Security Sensitive Data Flow Based on Weighted Deep Forest
The public security sensitive data flow exhibit high levels of concealment and complexity,making it dif-ficult to classify these data in the data mining process,resulting in the decline of data mining quality.Therefore,a dynamic mining method of public security sensitive data flow based on weighted depth forest is proposed.The local-ized differential privacy technology is employed to collect the privacy sensitive data flow of the public security depart-ment applications.After the sensitive data flow vector is obtained according to the maximum inter-class divergence,the mining features of public security sensitive data flow are extracted by calculating the best divergence.The dynamic mining of public security sensitive data flow is realized by calculating the category density of data flow with the mining features and the weighted depth forest combined,and dynamically classifying the data flow based on isolation factors.The proposed method proves to be effective in practical application.
weighted depth forestsensitive data flowdynamic miningdifferential privacymining featuresisolation factors