用强化学习动态调整云安全策略，提升防护效率。
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning
- 基于深度强化学习自适应优化防火墙与权限策略
- 入侵检测率92%（静态策略82%），响应时间缩短58%
- 适合关注云安全自动化与合规性的运维团队
云计算环境（如AWS）的安全性复杂且动态变化，静态安全策略已难以应对持续演进的威胁和资源弹性。本文提出一种基于强化学习（RL）的自适应安全策略管理框架，采用深度Q网络和近端策略优化等算法，持续学习并调整防火墙规则与身份访问管理（IAM）策略。该框架利用云遥测数据（AWS CloudTrail日志、网络流量、威胁情报）实时优化策略，在最大化威胁缓解与合规性的同时，最小化资源开销。实验表明，该方法入侵检测率达92%（静态策略为82%），事件检测与响应时间减少58%，同时保持高安全合规性与高效资源使用。结果验证了自适应强化学习在云安全策略管理中的有效性。
原文摘要 · Abstract (English)
The security of cloud environments, such as Amazon Web Services (AWS), is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity [1]. This paper addresses the limitations of static policies by proposing a security policy management framework that uses reinforcement learning (RL) to adapt dynamically. Specifically, we employ deep reinforcement learning algorithms, including deep Q Networks and proximal policy optimization, enabling the learning and continuous adjustment of controls such as firewall rules and Identity and Access Management (IAM) policies. The proposed RL based solution leverages cloud telemetry data (AWS Cloud Trail logs, network traffic data, threat intelligence feeds) to continuously refine security policies, maximizing threat mitigation, and compliance while minimizing resource impact. Experimental results demonstrate that our adaptive RL based framework significantly outperforms static policies, achieving higher intrusion detection rates (92% compared to 82% for static policies) and substantially reducing incident detection and response times by 58%. In addition, it maintains high conformity with security requirements and efficient resource usage. These findings validate the effectiveness of adaptive reinforcement learning approaches in improving cloud security policy management.
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