Research on Neural Network Algorithm in Risk Assessment of Network Security Spatial Data Assets
Chunzhi Meng, Liang Meng, Lina Chen, Dengbin Liao · 2024
With the rapid development of information technology, network security issues have become increasingly prominent, and accurate risk assessment of data assets in cyberspace is crucial. This article proposes a network security spatial data asset risk assessment method based on neural network algorithms, aiming to solve the problems of long assessment cycles, inaccurate results, and incomplete data in traditional assessment methods. This study first analyzes the limitations of traditional evaluation methods and proposes a new evaluation method based on this. This method effectively shortens evaluation time, improves evaluation accuracy, and enhances data comprehensiveness by constructing and training specific neural network models. The experimental results show that the proposed neural network-based method has significant advantages in risk assessment, especially in terms of speed and accuracy when processing large amounts of high-dimensional data. In addition, this article also explores future research directions, including algorithm optimization and hyperparameter adjustment, providing a new evaluation tool for the field of network security and laying the foundation for further technological improvement and application.