AI-Enhanced Security for Large-Scale Kubernetes Clusters: Advanced Defense and Authentication for National Cloud Infrastructure
DOI:
https://doi.org/10.5281/zenodo.14195743Keywords:
kubernetes security, artificial intelligence, large-scale clusters, national cloud infrastructureAbstract
This paper presents an AI-enhanced security framework for large-scale Kubernetes clusters, addressing the critical need for advanced defense and authentication mechanisms in national cloud infrastructures. The proposed system combines machine learning models for threats, policy creation, and intelligent resource allocation to provide security across the environment. An experiment simulating a 1,000-node Kubernetes cluster was used to evaluate the framework's performance over 30 days. The results showed a significant improvement over traditional security methods, including 99.97% threat detection accuracy, a false positive rate of 0.005%, and an 85% reduction in average response time to security threats. The framework exhibits excellent performance, maintaining consistent performance up to 10,000 nodes with only 7% degradation. Notably, the change resulted in a 27% improvement in overall stability throughout the trial. This research has a significant impact on the security of the country's airspace, providing effective protection against threats, insider attacks, and ongoing threats. The study concludes by discussing limitations and future research directions, emphasizing the need for real-world deployment and research on possible AI architectures. Better for limited spaces.
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Bringhenti, D., Sisto, R., & Valenza, F. (2023, June). Security automation for multi-cluster orchestration in Kubernetes. in IEEE 9th International Conference on Network Softwarization (NetSoft), pp. 480-485.
Kassi, M., & Hamouda, S. (2023, November). Machine learning: A new way for material resources orchestration in a large-scale V-RAN. in IEEE Tenth International Conference on Communications and Networking (ComNet), pp. 1-6.
Slipachuk, L., Toliupa, S., & Nakonechnyi, V. (2019, July). The process of critical infrastructure cyber security management using the integrated system of the national cyber security sector management in Ukraine. in 3rd International Conference on Advanced Information and Communications Technologies (AICT), pp. 451-454.
Shamim, M. S. I., Bhuiyan, F. A., & Rahman, A. (2020). Xi commandments of Kubernetes security: A systematization of knowledge related to Kubernetes security practices. IEEE Secure Development (SecDev), pp. 58-64.
Yu, P., Cui, V. Y., & Guan, J. (2021, March). Text classification by using natural language processing. in Journal of Physics: Conference Series, 1802(4), pp. 042010. IOP Publishing.
Ke, X., Li, L., Wang, Z., & Cao, G. (2024). A dynamic credit risk assessment model based on deep reinforcement learning. Academic Journal of Natural Science, 1(1), 20-31.
Zhu, Y., Yu, K., Wei, M., Pu, Y., & Wang, Z. (2024). AI-enhanced administrative prosecutorial supervision in financial big data: New concepts and functions for the digital era. Social Science Journal for Advanced Research, 4(5), 40-54.
Zhao, Fanyi, et al. (2024). Application of deep reinforcement learning for cryptocurrency market trend forecasting and risk management. Journal of Industrial Engineering and Applied Science, 2(5), 48-55.
Yuan, B., Cao, G., Sun, J., & Zhou, S. (2024). Optimising AI workload distribution in multi-cloud environments: A dynamic resource allocation approach. Journal of Industrial Engineering and Applied Science, 2(5), 68-79.
Zhan, X., Xu, Y., & Liu, Y. (2024). Personalized UI layout generation using deep learning: An adaptive interface design approach for enhanced user experience. Journal of Artificial Intelligence and Development, 3(1).
Zhou, S., Zheng, W., Xu, Y., & Liu, Y. (2024). Enhancing user experience in VR environments through AI-driven adaptive UI design. Journal of Artificial Intelligence General Science (JAIGS), 6(1), 59-82.
Wang, S., Zhang, H., Zhou, S., Sun, J., & Shen, Q. (2024). Chip floorplanning optimization using deep reinforcement learning. International Journal of Innovative Research in Computer Science & Technology, 12(5), 100-109.
Wei, M., Pu, Y., Lou, Q., Zhu, Y., & Wang, Z. (2024). Machine learning-based intelligent risk management and arbitrage system for fixed income markets: Integrating high-frequency trading data and natural language processing. Journal of Industrial Engineering and Applied Science, 2(5), 56-67.
Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). Distributed high-performance computing methods for accelerating deep learning training. Journal of Knowledge Learning and Science Technology, 3(3), 108-126.
Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.
Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of large language models in cloud computing: An empirical study using real-world data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69.
Wang, Shikai, Kangming Xu, & Zhipeng Ling. (2024). Deep learning-based chip power prediction and optimization: An intelligent EDA approach. International Journal of Innovative Research in Computer Science & Technology, 12(4), 77-87.
Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent classification and personalized recommendation of e-commerce products based on machine learning. arXiv preprint arXiv:2403.19345.
Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and optimization of intelligent recommendation system performance with cloud resource automation compatibility.
Zheng, H., Xu, K., Zhou, H., Wang, Y., & Su, G. (2024). Medication recommendation system based on natural language processing for patient emotion analysis. Academic Journal of Science and Technology, 10(1), 62-68.
Zheng, H., Wu, J., Song, R., Guo, L., & Xu, Z. (2024). Predicting financial enterprise stocks, and economic data trends using machine learning time series analysis. Applied and Computational Engineering, 87, 26–32.
Liu, B., & Zhang, Y. (2023). Implementation of seamless assistance with Google Assistant leveraging cloud computing. Journal of Cloud Computing, 12(4), 1-15.
Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-cloud complete: leveraging cloud computing for efficient large language model-based code completion. Journal of Artificial Intelligence General Science (JAIGS), 5(1), 295-326.
Li, P., Hua, Y., Cao, Q., & Zhang, M. (2020, December). Improving the restore performance via physical-locality middleware for backup systems. in Proceedings of the 21st International Middleware Conference, pp. 341-355.
Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). The impact of pricing schemes on cloud computing and distributed systems. Journal of Knowledge Learning and Science Technology, 3(3), 193-205.
Shang, F., Zhao, F., Zhang, M., Sun, J., & Shi, J. (2024). Personalized recommendation systems powered by large language models: Integrating semantic understanding and user preferences. International Journal of Innovative Research in Engineering and Management, 11(4), 39-49.
Sun, J., Wen, X., Ping, G., & Zhang, M. (2024). Application of news analysis based on large language models in supply chain risk prediction. Journal of Computer Technology and Applied Mathematics, 1(3), 55-65.
Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Detection of network security traffic anomalies based on machine learning KNN method. Journal of Artificial Intelligence General Science (JAIGS), 1(1), 209-218.
Ju, Chengru, & Yida Zhu. (2024). Reinforcement learning based model for enterprise financial asset risk assessment and intelligent decision making.
Yu, Keke, et al. (2024). Loan approval prediction improved by XGBoost model based on four-vector optimization algorithm.
Zhou, S., Sun, J., & Xu, K. (2024). AI-driven data processing and decision optimization in iot through edge computing and cloud architecture.
Sun, J., Zhou, S., Zhan, X., & Wu, J. (2024). Enhancing supply chain efficiency with time series analysis and deep learning techniques.
Zheng, H., Xu, K., Zhang, M., Tan, H., & Li, H. (2024). Efficient resource allocation in cloud computing environments using AI-driven predictive analytics. Applied and Computational Engineering, 82, 6-12.
Wang, S., Zheng, H., Wen, X., Xu, K., & Tan, H. (2024). Enhancing chip design verification through AI-powered bug detection in RTL code. Applied and Computational Engineering, 92, 27-33.
Li, H., Wang, G., Li, L., & Wang, J. (2024). Dynamic resource allocation and energy optimization in cloud data centers using deep reinforcement learning. Journal of Artificial Intelligence General Science (JAIGS), 1(1), 230-258.
Li, H., Sun, J., & Ke, X. (2024). AI-driven optimization system for large-scale kubernetes clusters: Enhancing cloud infrastructure availability, security, and disaster recovery. Journal of Artificial Intelligence General Science (JAIGS), 2(1), 281-306.
Xia, S., Wei, M., Zhu, Y., & Pu, Y. (2024). AI-driven intelligent financial analysis: enhancing accuracy and efficiency in financial decision-making. Journal of Economic Theory and Business Management, 1(5), 1-11.
Zhang, H., Lu, T., Wang, J., & Li, L. (2024). Enhancing facial micro-expression recognition in low-light conditions using attention-guided deep learning. Journal of Economic Theory and Business Management, 1(5), 12-22.
Wang, J., Lu, T., Li, L., & Huang, D. (2024). Enhancing personalized search with ai: a hybrid approach integrating deep learning and cloud computing. International Journal of Innovative Research in Computer Science & Technology, 12(5), 127-138.
Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative ai into financial market prediction for improved decision making. arXiv preprint arXiv:2404.03523.
Che, C., Zheng, H., Huang, Z., Jiang, W., & Liu, B. (2024). Intelligent robotic control system based on computer vision technology. arXiv preprint arXiv:2404.01116.
Ma, X., Zeyu, W., Ni, X., & Ping, G. (2024). Artificial intelligence-based inventory management for retail supply chain optimization: a case study of customer retention and revenue growth. Journal of Knowledge Learning and Science Technology, 3(4), 260-273.
Ni, X., Zhang, Y., Pu, Y., Wei, M., & Lou, Q. (2024). A personalized causal inference framework for media effectiveness using hierarchical bayesian market mix models. Journal of Artificial Intelligence and Development, 3(1).
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Copyright (c) 2024 Lin Li, Xiong Ke, Gaike Wang, Jiatu Shi
This work is licensed under a Creative Commons Attribution 4.0 International License.
