Research on the Application of Blockchain Technology in Ensuring Network Security
Keywords:
Blockchain technology, Network security, Data security sharing, Identity authentication, Smart Contract SecurityAbstract
With the rapid development of information technology, network security issues are becoming increasingly prominent, and traditional network security technologies are facing new challenges. The article aims to discuss the role of blockchain technology in ensuring network security, and analyze the advantages, challenges, and future trends faced by blockchain technology. This article first summarizes the definition, principles, and core characteristics of blockchain technology, and compares it with traditional network security technologies. Then, the article delves into the current applications and potential advantages of blockchain technology in network security from multiple perspectives, including data security sharing, authentication and access control, network attack protection, and smart contract security. At the same time, it points out the technical challenges, legal and ethical issues, and regulatory and policy deficiencies faced by the application of blockchain technology in network security, and finally provides relevant countermeasures and suggestions. Finally, a brief summary of the entire paper is made, highlighting the prospects and significance of blockchain applications in the field of network security, and pointing out future research directions and potential challenges, demonstrating the forward-looking and exploratory spirit of academic papers.
References
Guo, Y., & Tao, D. (2025). Modeling and Simulation Analysis of Robot Environmental Interaction. Artificial Intelligence Technology Research, 2(8).
We, X., Lin, S., Pruś, K., Zhu, X., Jia, X., & Du, R. (2025). Towards Intelligent Monitoring of Anesthesia Depth by Leveraging Multimodal Physiological Data. International Journal of Advance in Clinical Science Research, 4, 26–37. Retrieved from https://www.h-tsp.com/index.php/ijacsr/article/view/158
Yang, Y. (2025). Research on Site Reliability Optimization Technology Based on Synthetic Monitoring in Cloud Environments.
Tang, Z., Feng, Y., Zhang, J., & Wang, Z. (2026). SVD-BDRL: A trustworthy autonomous driving decision framework based on sparse voxels and blockchain enhancement. Alexandria Engineering Journal, 134, 433-446.
Lu, K., Sui, Q., Chen, X., & Wang, Z. (2025). NeuroDiff3D: a 3D generation method optimizing viewpoint consistency through diffusion modeling. Scientific Reports, 15(1), 41084.
Zhang, T. (2025). From Black Box to Actionable Insights: An Adaptive Explainable AI Framework for Proactive Tax Risk Mitigation in Small and Medium Enterprises.
Xie, J., Zhang, L., Cheng, L., Yao, J., Qian, P., Zhu, B., & Liu, G. (2025). MARNet: Multi-scale adaptive relational network for robust point cloud completion via cross-modal fusion. Information Fusion, 103505.
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Y. Zhang, Z. Tian and H. Hua, "Design of an Autonomous Vehicle Speed Control System Based on a PID Controller," 2025 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, 2025, pp. 491-495, doi: 10.1109/AEECA65693.2025.00092.
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Sultan, N., Patwar, N., Wei, X., Chew, J., Liu, J., & Du, R. (2026). FedGuard: A Robust Federated AI Framework for Privacy-Conscious Collaborative AML, Inspired by DARPA GARD Principles. International Academic Journal of Social Science, 2, 1–16. https://doi.org/10.5281/zenodo.18253151
Zhu, Y., Yu, W., & Li, R. (2025). SAGCN: A spatiotemporal attention-weighted graph convolutional network with IoT integration for adolescent tennis motion analysis. Alexandria Engineering Journal, 128, 652-662.
Yang, J., Wang, Z., & Chen, C. (2024). GCN-MF: A graph convolutional network based on matrix factorization for recommendation. Innovation & Technology Advances, 2(1), 14–26. https://doi.org/10.61187/ita.v2i1.30
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Qingwang Jie, Xueyong Zhou
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
