The Application of Artificial Intelligence Technology in Big Data Network Security Defense
Keywords:
Artificial intelligence, Technology, Big data network, Security defense, ApplicationAbstract
Artificial intelligence technology, as a highly promising technology among various technologies, its application in big data network security defense has extremely important practical significance for protecting the security of big data. This article explores the key applications and significant advantages of artificial intelligence technology in big data network security defense. Through in-depth analysis of multiple aspects such as fuzzy data processing, learning and reasoning abilities, network defense assistance, intelligent firewalls, intrusion detection, neural network systems, data mining and fusion, spam prevention, and artificial immune technology, this study reveals how artificial intelligence can effectively enhance the security and defense capabilities of big data networks, providing theoretical basis and practical guidance for building a more stable network security system.
References
HOU, R., JEONG, S., WANG, Y., LAW, K. H., & LYNCH, J. P. (2017). Camera-based triggering of bridge structural health monitoring systems using a cyber-physical system framework. Structural Health Monitoring 2017, (shm).
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.
Y. Zhang, Z. Bai and Q. Luo, "AI-Driven Cloud Computing Data Security Monitoring and Response System," 2025 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, 2025, pp. 817-821, doi: 10.1109/AEECA65693.2025.00148.
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.
Peng, Qucheng, Chen Bai, Guoxiang Zhang, Bo Xu, Xiaotong Liu, Xiaoyin Zheng, Chen Chen, and Cheng Lu. "NavigScene: Bridging Local Perception and Global Navigation for Beyond-Visual-Range Autonomous Driving." arXiv preprint arXiv:2507.05227 (2025).
Peng, Qucheng, Ce Zheng, and Chen Chen. "A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yanrong Tong
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
