Key points are not available for this paper at this time.
In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.
Yì Wáng (Mon,) studied this question.