用博弈论与符号学习，自动攻防测试医疗AI系统漏洞
ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing
- 结合博弈论与神经符号系统，实现自适应渗透测试
- 实验证明可有效识别并防御多种对抗性攻击策略
- 适合医疗等关键基础设施的自动化安全防护
人工智能在医疗等关键基础设施中的应用引入了新漏洞，传统人工渗透测试已无法满足需求。本文提出ADAPT框架，融合博弈论与神经符号方法，针对智能医疗网络设计分布式、自适应的自动化渗透测试方案。通过医疗系统案例研究验证方法可行性，实现基于学习的风险评估。数值实验表明，该框架能有效识别并应对多种对抗性战术技术，提升系统安全防御能力。
原文摘要 · Abstract (English)
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.
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