Research on Network Security Evaluation Model Based on AHP and BP Neural Network

Abstract

The Internet's sharing and openness have made information interaction more vulnerable to security risks. As a result, a comprehensive evaluation of the security of computer network systems has become a more effective means of preventing various network security problems. In recent years, there have been many network security evaluation methods proposed to address this issue, but not all of them are effective. Therefore, this paper analyzes existing network security evaluation methods and proposes a new model based on BP neural network and AHP jointly. The proposed model combines the advantages of BP neural network and hierarchical analysis (AHP) to provide a comprehensive and accurate evaluation of network security. The BP neural network is used to evaluate the risk level of each security factor, while AHP is used to calculate the weights of each security factor. The weights reflect the relative importance of each factor in determining the overall security level of the network. To verify the applicability of the proposed model, empirical research is conducted. The results demonstrate that the model can effectively evaluate network security comprehensively. The model's accuracy and effectiveness make it a promising approach to evaluate the security of computer network systems. Additionally, it can help in developing strategies to enhance network security by identifying potential vulnerabilities and assessing the effectiveness of security measures implemented. In conclusion, the model provides a useful tool for organizations to manage network security effectively.