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 modle 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.
This paper analyzes the use of CBERS (China-Brazil Earth Resources Satellite) high resolution images to map urban limits of a small city in the Amazon Region of Brazil. The use of Sino-Brazilian satellite images to study small cities of the Brazilian Amazon Region is also discussed. A HCR image was used to classify the image of the study area into four classes: urban, forest, clouds, and shadows, by visual interpretation. This map was compared with another map produced with unsupervised classification. A confusion matrix was produced to evaluate the accuracy of unsupervised classification. Comparison between the reference map (visual interpretation) and the unsupervised classification map verified that 82.8% of the areas were classified correctly. This ability to recognize and delimitate small cities in Amazonia demonstrates the quality of the CBERS – HCR image.