Research On Digital Secret Level Determination Technology Based On Machine Learning

Since the Nineteenth National Congress,the work of confidentiality has increasingly become a key work to safeguard national interests.As a key part of security work,the determination of secret level has gradually become the focus of people's attention.Traditional artificial secret level determination methods have some shortcomings,such as poor controllability,weak adaptability,low accuracy and low efficiency.With the rise of the field of artificial intelligence,the use of computer technology to establish digital secret level determination technology has become the focus of current research in the field of confidentiality.In this paper,support vector machine(SVM)classification algorithm and minimum maximum modular network(M3)algorithm in machine learning theory are thoroughly studied and analyzed.Combined with machine support vector machine(SVM)classification algorithm and minimum maximum modular network(M3)algorithm,the minimum maximum support vector machine(M3-SVMs)secret level is proposed identify technology.The performance of M3-SVMs(H)secret level determination technology is validated by designing related experiments.By comparing the efficiency(time-consuming)and accuracy(F1 value)of different densification techniques,it is concluded that the Minimum and Maximum Support Vector Machine(M3-SVMs(H)secret level determination technology has good efficiency and accuracy.It can be effectively applied to secret level determination.