MEGA-PT：一种用于敏捷渗透测试的元博弈框架
MEGA-PT: A Meta-Game Framework for Agile Penetration Testing
AI总结：
本文提出元博弈渗透测试框架MEGA-PT，通过微观战术博弈与宏观策略过程实现分布式、自适应的敏捷自动化渗透测试，有效解决维度灾难与可扩展性问题。
摘要：
渗透测试是应对日益升级的网络安全事件的重要主动防御手段。传统的人工渗透测试方法耗时、耗费资源且容易出现人为错误。当前自动化渗透测试的趋势也不切实际，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。为了解决这些问题，我们提出了MEGA-PT，一个元博弈渗透测试框架，其特点包括用于节点级局部交互的微观战术博弈和用于网络级攻击链的宏观策略过程。微观和宏观层面的建模实现了分布式、自适应、协作和快速的渗透测试。MEGA-PT为各种安全方案提供了敏捷解决方案，包括最优局部渗透计划、紫队解决方案和风险评估，为指导未来自动化渗透测试提供了基本原则。我们的实验通过提供改进的防御策略以及对局部和网络层面变化的适应性，证明了我们模型的有效性和敏捷性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
