用强化学习实现DevSecOps自动修复，提升安全响应速度与检测率。
AutoGuard: A Self-Healing Proactive Security Layer for DevSecOps Pipelines Using Reinforcement Learning
- 基于强化学习动态优化安全策略，实时响应异常
- 检测准确率提升22%，平均恢复时间缩短38%
- 适合需要持续防护的自动化部署团队
现代DevSecOps流水线需应对持续集成与部署环境中的安全演变。现有方法如规则检测和静态漏洞扫描难以适应系统变化，导致响应延迟，使组织暴露于新兴攻击向量。为此，我们提出AutoGuard——一种基于强化学习（RL）的自愈式安全框架，旨在主动保护DevSecOps环境。该框架持续监控流水线活动，预判异常并主动修复。其智能体通过奖励驱动的学习机制，不断优化决策策略，提升实时预防、检测与响应能力。在模拟CI/CD环境中测试表明，相比传统方法，AutoGuard可提升威胁检测准确率22%，将平均恢复时间（MTTR）缩短38%，显著增强整体韧性。
原文摘要 · Abstract (English)
Contemporary DevSecOps pipelines have to deal with the evolution of security in an ever-continuously integrated and deployed environment. Existing methods,such as rule-based intrusion detection and static vulnerability scanning, are inadequate and unreceptive to changes in the system, causing longer response times and organization needs exposure to emerging attack vectors. In light of the previous constraints, we introduce AutoGuard to the DevSecOps ecosystem, a reinforcement learning (RL)-powered self-healing security framework built to pre-emptively protect DevSecOps environments. AutoGuard is a self-securing security environment that continuously observes pipeline activities for potential anomalies while preemptively remediating the environment. The model observes and reacts based on a policy that is continually learned dynamically over time. The RL agent improves each action over time through reward-based learning aimed at improving the agent's ability to prevent, detect and respond to a security incident in real-time. Testing using simulated ContinuousIntegration / Continuous Deployment (CI/CD) environments showed AutoGuard to successfully improve threat detection accuracy by 22%, reduce mean time torecovery (MTTR) for incidents by 38% and increase overall resilience to incidents as compared to traditional methods. Keywords- DevSecOps, Reinforcement Learning, Self- Healing Security, Continuous Integration, Automated Threat Mitigation
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