用元博弈框架实现快速自适应渗透测试
MEGA-PT: A Meta-Game Framework for Agile Penetration Testing
- 分层建模：微观战术游戏处理节点级攻击，宏观策略统筹全网链路
- 实验验证：在动态网络中实现更快响应与更优防御策略
- 适合安全团队用于自动化渗透与红蓝对抗演练
渗透测试是应对日益严峻网络安全事件的重要主动防御手段。传统人工渗透测试耗时长、资源消耗大且易出错；现有自动化渗透测试面临维度灾难、可扩展性差及难以适应网络变化等挑战。为此，我们提出 MEGA-PT，一种元博弈渗透测试框架，包含节点级局部交互的微观战术游戏和网络级攻击链的宏观策略过程。该分层建模支持分布式、自适应、协作式与快速渗透测试，适用于多种安全场景，包括最优本地渗透方案、红蓝协同（purple teaming）及风险评估，为未来自动化渗透测试提供基础指导原则。实验表明，该模型在局部与网络层面均展现出更强的适应性与有效性的改进防御策略。
原文摘要 · Abstract (English)
Penetration testing is an essential means of proactive defense in the face of escalating cybersecurity incidents. Traditional manual penetration testing methods are time-consuming, resource-intensive, and prone to human errors. Current trends in automated penetration testing are also impractical, facing significant challenges such as the curse of dimensionality, scalability issues, and lack of adaptability to network changes. To address these issues, we propose MEGA-PT, a meta-game penetration testing framework, featuring micro tactic games for node-level local interactions and a macro strategy process for network-wide attack chains. The micro- and macro-level modeling enables distributed, adaptive, collaborative, and fast penetration testing. MEGA-PT offers agile solutions for various security schemes, including optimal local penetration plans, purple teaming solutions, and risk assessment, providing fundamental principles to guide future automated penetration testing. Our experiments demonstrate the effectiveness and agility of our model by providing improved defense strategies and adaptability to changes at both local and network levels.
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