Research Article
Research on the Application of Neural Networks in the Identification and Classification of Data Assets in Network Security Space
@INPROCEEDINGS{10.1007/978-3-031-96944-7_6, author={Chunzhi Meng and Anni Huang and Siwei Li and Junbing Pan}, title={Research on the Application of Neural Networks in the Identification and Classification of Data Assets in Network Security Space}, proceedings={Computer and Information Security. First World Conference, WCCIS 2024, Kuala Lumpur, Malaysia, September 20--22, 2024, Proceedings}, proceedings_a={WCCIS}, year={2026}, month={11}, keywords={Deep learning Neural network Network security Classification of data assets Hybrid model}, doi={10.1007/978-3-031-96944-7_6} }- Chunzhi Meng
Anni Huang
Siwei Li
Junbing Pan
Year: 2026
Research on the Application of Neural Networks in the Identification and Classification of Data Assets in Network Security Space
WCCIS
Springer
DOI: 10.1007/978-3-031-96944-7_6
Abstract
The continuous evolution and complexity of cyber threats make the identification and classification of data assets in the cybersecurity space crucial. This study uses deep learning techniques, especially neural network models, to conduct in-depth analysis and modeling of network threat behavior, aiming to improve the security and reliability of data assets. Firstly, this article provides an overview of the current application status and potential value of neural networks in the field of network security. Aiming at the shortcomings of existing neural networks in data classification accuracy and computational efficiency, a hybrid neural network model based on incremental learning strategy (combining CNN and RNN) is proposed to improve the recognition and classification efficiency of network security spatial data assets. The experimental results show that the proposed improvement method has significantly improved the accuracy of data classification and recognition, and has important practical application value in the field of network security.