Multiagent penetration testing based on AIRL

This paper explores the application of a multi-agent approach based on the Adversarial Inverse Reinforcement Learning (AIRL) method for penetration testing in information systems. Theoretical aspects of multi-agent AIRL are discussed, including the ability to model complex, multi-stage attacks, coordinate agent actions, and learn with partial observability, which accounts for limitations in information access. The practical application of this approach will demonstrate its effectiveness in identifying vulnerabilities, providing a deeper and more accurate security analysis.