Network security situation prediction method based on improved BPNN (back propagation neural network)

Abstract

The invention relates to the technical field of network security evaluation, in particular to a network security situation prediction method based on a combination of the chaos theory and a neural network. The method comprises the following steps: carrying out processing of normalized network security situation value sequence sets through the mutual information method and the cao method to obtain the optimum embedded dimensions of network security situation sample values, carrying out phase-space reconstruction, and analyzing the maximum Lyapunov exponent of reconstructed samples to determine whether the evaluated samples have chaos predictability or not; determining the numbers of nodes of an output layer and a hidden layer of a BPNN according to characteristics of a nonlinear time sequence and experience; carrying out parameter optimization through an improved firefly algorithm, so as to determine network weights and offset values and establish a network security situation prediction model; and inputting test set samples into the BP neutral network for prediction, and carrying out denormalization of obtained prediction values. The method provided by the invention has the advantages that a network security situation can be more precisely predicted, and the network security situation prediction convergence rate can be increased.

Description

technical field [0001] The invention relates to the technical field of network security assessment, in particular to a security situation prediction method based on an improved back propagation neural network (BPNN). Background technique [0002] In recent years, with the advent and popularization of the era of mobile Internet and smart terminals, people's online behaviors have become more frequent, and the scale of marketing has become larger and larger. Various social networks have formed a complex, heterogeneous large-scale network. However, due to the characteristics of mobility, scalability, large-scale, ubiquity and other characteristics of communication networks, while the network penetrates into people's social life, it has also become the primary target of hacker attacks, resulting in a continuous and rapid increase in the number of network security vulnerabilities. Therefore, the security problem will definitely become the primary problem to be solved in the future...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to provide a network security situation prediction method based on improved BPNN, which is used to solve the problem that the existing human-specified input dimension leads to network unpredictability and the network is easy to fall into local optimum, which leads to low prediction accuracy of network security situation

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