Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908702597971968 |
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| author | Wang, Yuqing Yang, Xiao |
| author_facet | Wang, Yuqing Yang, Xiao |
| contents | Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection. Traditional security mechanisms, based on rule matching and feature recognition, struggle to adapt to evolving attack strategies. This paper proposes an adaptive security protection framework leveraging deep learning to construct a multi-layered defense architecture. The proposed system is evaluated in a real-world business environment, achieving a detection accuracy of 97.3%, an average response time of 18 ms, and an availability rate of 99.999%. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, response efficiency, and resource utilization, offering a novel and effective approach to cloud computing security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms Wang, Yuqing Yang, Xiao Cryptography and Security Artificial Intelligence Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection. Traditional security mechanisms, based on rule matching and feature recognition, struggle to adapt to evolving attack strategies. This paper proposes an adaptive security protection framework leveraging deep learning to construct a multi-layered defense architecture. The proposed system is evaluated in a real-world business environment, achieving a detection accuracy of 97.3%, an average response time of 18 ms, and an availability rate of 99.999%. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, response efficiency, and resource utilization, offering a novel and effective approach to cloud computing security. |
| title | Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2502.17801 |