用深度学习构建自适应云安全防御，检测准确率达97.3%
Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms
- 基于深度学习构建多层防御架构，动态适应攻击变化
- 真实环境测试中检测准确率97.3%，响应时间仅18毫秒
- 适合需要高可用性与快速响应的云服务安全团队
云计算环境日益面临分布式拒绝服务（DDoS）攻击和SQL注入等安全威胁。传统基于规则匹配与特征识别的安全机制难以适应不断演变的攻击策略。本文提出一种自适应安全防护框架，利用深度学习构建多层防御体系。该系统在真实业务环境中进行评估，检测准确率达到97.3%，平均响应时间为18毫秒，可用性达99.999%。实验结果表明，该方法显著提升了检测准确率、响应效率与资源利用率，为云安全提供了新颖且有效的解决方案。
原文摘要 · Abstract (English)
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.
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