Title: ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing Show Chinese title

Title: ADAPT：自动化分布式自适应渗透测试的博弈论与神经符号框架

Abstract: The integration of AI into modern critical infrastructure systems, such as healthcare, has introduced new vulnerabilities that can significantly impact workflow, efficiency, and safety. Additionally, the increased connectivity has made traditional human-driven penetration testing insufficient for assessing risks and developing remediation strategies. Consequently, there is a pressing need for a distributed, adaptive, and efficient automated penetration testing framework that not only identifies vulnerabilities but also provides countermeasures to enhance security posture. This work presents ADAPT, a game-theoretic and neuro-symbolic framework for automated distributed adaptive penetration testing, specifically designed to address the unique cybersecurity challenges of AI-enabled healthcare infrastructure networks. We use a healthcare system case study to illustrate the methodologies within ADAPT. The proposed solution enables a learning-based risk assessment. Numerical experiments are used to demonstrate effective countermeasures against various tactical techniques employed by adversarial AI. Show Chinese abstract

Abstract: 将人工智能整合到现代关键基础设施系统（如医疗保健）中，引入了可能显著影响工作流程、效率和安全性的新漏洞。 此外，增加的连接性使得传统的基于人力的渗透测试不足以评估风险并制定补救策略。 因此，迫切需要一种分布式、自适应且高效的自动化渗透测试框架，该框架不仅能够识别漏洞，还能提供对策以增强安全态势。 本研究提出了ADAPT，这是一种基于博弈论和神经符号的自动化分布式自适应渗透测试框架，专门设计用于解决人工智能赋能的医疗保健基础设施网络的独特网络安全挑战。 我们使用一个医疗保健系统案例研究来说明ADAPT中的方法。 所提出的解决方案实现了基于学习的风险评估。 数值实验用于展示对对手人工智能采用的各种战术技术的有效对策。