Research on Security Defense Methods for Big Data Centers
DOI:
https://doi.org/10.54097/3hc45h55Keywords:
Big Data Center, Defense Architecture, Traffic Analysis EngineAbstract
By integrating and analyzing massive amounts of data, big data centers can effectively address key issues in multiple fields. However, as the scale and importance of big data centers continue to grow, security issues such as high-risk vulnerabilities exposed due to inadequate security configurations in physical computing facilities, malicious attacks stemming from lax access control at cloud boundaries, effectiveness issues in virtual resource isolation, and insufficient security protection measures for cloud platforms have become increasingly prominent. Based on the existing big data center architecture, this paper designs a security protection framework from four dimensions: physical computing facilities, cloud boundary access zones, cloud tenant zones, and cloud platforms. It integrates security protection functions and measures based on traffic analysis, including access control and risk assessment.
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References
[1] Liu, F. X. (2024). Data security in big data centers: Regulatory and compliance research.
[2] Wan, Q. (2019). Practices in security architecture design for cloud data centers. Information & Communications.
[3] McKeown, N., Anderson, T., Balakrishnan, H., & et al. (2008). OpenFlow. ACM SIGCOMM Computer Communication Review, 38(2), 69 74. https://doi.org/10. 1145/ 1341421. 134 1426.
[4] Chiosi, M., Clarke, D., Willis, P., & et al. (2012). Network functions virtualisation introductory white paper [White paper].
[5] Huang, M., Luo, W. B., & Wan, X. (2019). Research on network security of campus network. Journal of Physics: Conference Series, 1187(4), 042113.
[6] Yusof, M. A. M., Ali, F. H. M., & Darus, M. Y. (2018). Detection and defense algorithms of different types of DDoS attacks using machine learning. In International Conference on Computational Science and Technology (pp. 370 379). Springer.
[7] Wu, B., Zhong, E. H., Tan, B., & et al. (2014). Crowdsourced time sync video tagging using temporal and personalized topic modeling. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 721 730). Association for Computing Machinery.
[8] Lv, G. Y., Xu, T., & Chen, E. H. (2016). Reading the videos: Temporal labeling for crowdsourced time sync videos based on semantic embedding. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.
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