Dynamic Maintenance of Network Space Asset Library- Application of Neural Network Algorithms in Asset Change Perception and Confirmation Process

The dynamic maintenance of the cyberspace asset library is crucial for ensuring information security and business continuity. With the rapid development of the network environment, traditional manual maintenance methods can no longer meet the needs of real-time updates. Therefore, this study proposes an automated dynamic maintenance method based on neural network algorithms. This method aims to improve maintenance efficiency and accuracy by automatically perceiving asset changes and utilizing deep learning techniques for confirmation. The study first analyzed the current situation and challenges of dynamic maintenance of cyberspace asset libraries, and then introduced a neural network-based dynamic maintenance method, which includes key steps such as data collection, anomaly perception, preliminary screening, deep learning confirmation, and manual review. The experimental results show that the proposed method has significant performance improvement in asset change detection and confirmation, with high levels of accuracy, precision, recall, and F1 score. Finally, the article summarizes the research findings and provides prospects for future work directions, including further optimization of neural network structure and parameters, multi-source data fusion, and adaptation to the development of emerging technologies.