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

TL;DR

ADAPT is a novel framework combining game theory and neuro-symbolic AI to automate and adapt penetration testing in AI-enabled healthcare networks, improving vulnerability detection and countermeasure development.

Contribution

It introduces a distributed, adaptive, and neuro-symbolic approach for automated penetration testing tailored for complex AI-driven healthcare systems.

Findings

Effective countermeasures demonstrated against adversarial AI tactics

Learning-based risk assessment enhances vulnerability detection

Framework applicable to real-world healthcare cybersecurity challenges

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.