The performance of artificial intelligence (AI) algorithms in practice depends on the realism and correctness of the data, models, and feedback (labels or rewards) provided to the algorithm. This paper discusses methods for improving the realism and ecological validity of AI used for autonomous cyber defense by exploring the potential to use Inverse Reinforcement Learning (IRL) to gain insight into attacker actions, utilities of those actions, and ultimately decision points which cyber deception could thwart. The Tularosa study, as one example, provides experimental data of real-world techniques and tools commonly used by attackers, from which core data vectors can be leveraged to inform an autonomous cyber defense system.

翻译：人工智能算法的实际表现取决于向算法提供的数据、模型和反馈(标签或奖赏)的实际情况和正确性。本文讨论了如何通过探索利用反强化学习(IRL)了解攻击者的行动、这些行动的效用以及最终网络欺骗可能挫败的决定点的可能性,从而改进用于自主网络防御的人工智能的现实性和生态有效性。Tularosa研究举例介绍了攻击者通常使用的真实世界技术和工具的实验数据,从中可以利用核心数据矢量为自主网络防御系统提供信息。