Research on the application of neural networks in risk assessment of network security space data assets

This study aims to explore the application of neural network technology in the field of network security, especially in data asset risk assessment. The article first introduces the basic principles of neural networks and analyzes their key role in risk assessment of data assets in cyberspace. In response to the challenges of existing methods, such as insufficient data, overfitting, model selection, and classification problems, this paper proposes a series of innovative solutions, including data preprocessing techniques, model selection optimization, the combination of supervised and unsupervised learning algorithms, and parameter adjustment strategies. The application of these technologies significantly improves the prediction accuracy and generalization ability of neural networks, providing a theoretical basis and practical guidance for building an advanced, reliable, and efficient network security assessment system. In addition, this study also verified the effectiveness of the proposed method through experiments and demonstrated its performance advantages on different datasets by comparing it with existing technologies.