THE USE OF NEURAL NETWORK IN DEFENSE AUDIT NONLINEAR DYNAMIC PROCESSING UNDER THE BACKGROUND OF BIG DATA

Abstract

The purpose is to further explore the application effect of the neural network algorithm in defense audit and improve the user information security performance. Based on the relevant theoretical basis of neural network in machine learning, the back propagation neural network (BPNN) algorithm model is constructed and optimized. Moreover, by comparing with the classification and prediction effect of the decision tree method, the application effect of BPNN is further clarified. Through statistical analysis, a total of six risk users are screened out. The test data are classified into non-risk user group and risk user group to study the prediction of classification. The specific results are as follows. The prediction accuracy of non-risk group is 99% by using the BPNN algorithm and that is improved to 99.5% by using the optimized BPNN; for risk group, the prediction accuracy of BPNN is only 50% and that of optimized BPNN is 83.3%. Meanwhile, the prediction error rate of the BPNN algorithm is significantly lower than that of the decision tree algorithm, which further verifies the good application effect of the BPNN algorithm. This study can provide scientific and effective reference for the follow-up research of defense audit.