Construction of Emergency Response Mechanism and Data Protection System in Network Security

In order to improve the accuracy of automatic detection of malicious code, this paper focuses on the “texture” features of malicious code and the characteristics of different types of malicious code, which are also different, and uses them for the automatic detection of unknown malicious code by using the four machine learning algorithms of KNN, RF, NB and SVM to perform single-feature detection and multi-feature (GLCM, LBP and ngram feature merging) detection respectively. Four machine learning algorithms, namely KNN, RF, NB, and SVM, are used to perform single-feature detection and multi-feature (GLCM, LBP, and n-gram feature merging) detection respectively, and analyze the accuracy of the spatial relationship feature-oriented malicious code detection scheme. A multi-version oriented data protection model is proposed for the data storage space, data version, quantity management and recovery requirements involved in service emergency response. The relative performance errors between its data protection scheme and the plaintext scheme and the simple add noise scheme are analyzed. In all four machine learning algorithms, the detection rate of fused features is higher than that of single features, and the maximum difference can reach more than 60%. When takes the value of 9 or 3, the data privacy protection algorithm, the plaintext algorithm, and the noise-only addition algorithm in this paper have similar accuracy rates. With proper noise selection, this paper’s scheme has good performance in real simulation.