Method of Risk Assessment Based on Classified Security Protection and Fuzzy Neural Network

Risk assessment of information security is an important assessment method in the process of detecting potential threats and vulnerabilities. Select methods of risk assessment based on the requirements and the security level of organizational or enterprise information system. The general assessment methods simply calculate the risk value, In this paper, we propose a risk assessment model based on classified security protection. We also build a model combined fuzzy theory and BP neural network, so that the learn capability and the expression capability can be improved. Firstly, we form a risk elements set according to the classified criteria for security protection. Secondly, we quantitate the risk factors with fuzzy theory. Thirdly, we take the results the output of multi-level fuzzy system as the input of BP neural network. According to experiment testing, the risk evaluation model can estimate risk level of the information security accurately and real-timely.