A Study on the Application of Big Data Technologies in Computer Network Intrusion Detection Systems

Authors

  • Xavier Daimari Research Scholar, Department of Computer Science and Engineering, Punjabi University, Patiala, India

Keywords:

Big data technology, Computer network intrusion detection, Application

Abstract

With the continuous advancement of technological capabilities, computer and information technologies have undergone sustained development, leading to the emergence of big data technology. Big data technology encompasses a set of techniques for collecting, analyzing, organizing, and storing diverse types of information data. It enables effective management of various information categories and, when applied to computer network intrusion detection, enhances both the accuracy and precision of network security systems while facilitating the automation and intelligent operation of intrusion detection processes. Consequently, this integration holds significant practical value in ensuring the safe and stable operation of computer networks. Big data technology

References

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.

Sun, Lingxin. "AI-Assisted UI Design: Enhancing Efficiency and Creativity through Generative Tools." Journal of Computer Technology and Applied Mathematics 3.1 (2026): 19-27.

Liu, Ting. "Volatility Forecasting and Early-Warning Market Stress Detection: A Leakage-Safe Evaluation with Tree Ensembles and Transformers." (2026).

Yi, X. (2025, October). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1602-1607).

Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.

Tian, Q., Wang, Z., & Cui, X. (2024). Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv preprint arXiv:2409.13626.

Ximeng, Y., & Yiming, Z. (2026). Offline Conservative RL for Transaction Authorization: Smartly Balancing Fraud Risk and Customer Friction. Journal of Economic Theory and Business Management, 3(1), 1-9.

Zhao, S., Lin, Y., Yang, X., Lu, Q., Xue, H., & Jiang, G. (2025). Optimization of Deep Learning Models for Dynamic Market Behavior Prediction. arXiv preprint arXiv:2511.19090.

Yang, X., Xue, H., Hu, Q., & Zhang, Y. (2025, October). Design of a full-cycle intelligent risk control system for pre-loan, mid-loan, and post-loan lending: AI-driven closed-loop management of online credit security. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1022-1027).

Shen, Zepeng, et al. "Research on Application of Whale Optimization Algorithm in Financial Payment Fraud Detection." 2025 4th International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID). IEEE, 2025.

Li, W. (2026). Optimizing AI-Driven Bid Pricing Models for Non-Standard Automation Projects: Leveraging Historical Financial Data and Machine Learning Algorithms.

Z. Ren, "A Novel Feature Fusion-Based and Complex Contextual Model for Smoking Detection," 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE), Guangzhou, China, 2024, pp. 1181-1185, doi: 10.1109/CISCE62493.2024.10653351.

Zhou, Z. (2026). Bottleneck Diagnosis in International Automotive Sales Funnels Using Gradient Boosting Trees: Evidence from Cross-Regional Team Efficiency Evaluation. Journal of Computer Technology and Applied Mathematics, 3(1), 11-18.

Wensi, L. (2026). AI-Enabled Data Visualization Marketing for Automated Production Lines: Building Customer Trust and Improving Lead-to-Order Conversion. Academic Journal of Natural Science, 3(1), 8-13.

Published

2026-04-10

How to Cite

Daimari, X. (2026). A Study on the Application of Big Data Technologies in Computer Network Intrusion Detection Systems. Journal of Artificial Intelligence and Information, 3, 1–5. Retrieved from https://www.woodyinterpub.com/index.php/jaii/article/view/318