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.

翻译：面对日益升级的网络安全事件，渗透测试是一种重要的主动防御手段。传统的手动渗透测试方法耗时、资源密集且易受人为错误影响。当前自动化渗透测试的发展趋势同样不切实际，面临着维度灾难、可扩展性问题以及缺乏对网络变化的适应性等重大挑战。为解决这些问题，我们提出了MEGA-PT，一种元博弈渗透测试框架，其特点在于包含用于节点级局部交互的微观战术博弈以及用于全网攻击链的宏观策略过程。微观与宏观层面的建模实现了分布式、自适应、协同且快速的渗透测试。MEGA-PT为多种安全方案提供了敏捷解决方案，包括最优局部渗透计划、紫队协同方案以及风险评估，为未来自动化渗透测试提供了指导性的基本原理。我们的实验通过展示在局部和网络层面改进的防御策略及对变化的适应能力，证明了模型的有效性与敏捷性。