Research on network security spatial data asset protection technology based on deep learning

In the digital age, the protection of data assets in the enterprise cybersecurity space has become critical. This paper explores a deep learning-based data asset protection technique that aims to solve the challenges encountered by traditional network security technologies when dealing with complex network data. In this study, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were used to effectively classify and identify network data through supervised learning, which significantly improved the protection efficiency of data assets. The experimental results reveal the advantages of deep learning technology in data feature extraction, unknown attack identification and abnormal behavior detection, and prove its potential to improve the accuracy and robustness of network security protection. The results show that the deep learning method not only enhances the protection of data assets, but also combines with traditional defense strategies to form a more complete network security protection system.