Author(s)

Ruiqi Peng, Sirui Liu, Tianling Li

Corresponding Author

Ruiqi Peng

Abstract

In view of the uncertainty and complexity of information security risk assessment and the limitation of traditional mathematical methods in assessing information security risk grade, an information security risk assessment model based on fuzzy wavelet neural network is established by organically combining artificial neural network theory, wavelet analysis and fuzzy evaluation method. The fuzzy evaluation method is used to quantify the index of risk factors, and the output of fuzzy system is taken as the input of neural network, and the fuzzy wavelet neural network is constructed and trained [1]. The simulation results show that the fuzzy wavelet neural network model can quantitatively evaluate the risk factor level of information system, and solve the defects of subjective arbitrariness and fuzzy conclusion existing in the existing evaluation methods. Compared with BP neural network, the fuzzy wavelet neural network model has higher fitting accuracy and faster convergence speed.

Keywords

Wavelet neural network, Computers, Information security