We present a model-based approach to learning robust runtime monitors for autonomous systems. Runtime monitors play a crucial role in raising the level of assurance by observing system behavior and predicting potential safety violations. In our approach, we propose to capture a system's (stochastic) behavior using interval Hidden Markov Models (iHMMs). The monitor then uses this learned iHMM to derive risk estimates for potential safety violations. The paper makes three key contributions: (1) it provides a formalization of the problem of learning robust runtime monitors, (2) introduces a novel framework that uses conformance-testing-based refinement for learning robust iHMMs with convergence guarantees, and (3) presents an efficient monitoring algorithm for computing risk estimates over iHMMs. Our empirical results demonstrate the efficacy of monitors learned using our approach, particularly when compared to model-free monitoring approaches that rely solely on collected data without access to a system model.

翻译：我们提出了一种基于模型的方法，用于学习自主系统的鲁棒运行时监控器。运行时监控器通过观察系统行为并预测潜在的安全违规，在提升保障水平方面发挥着至关重要的作用。在我们的方法中，我们提出使用区间隐马尔可夫模型来捕获系统的（随机）行为。随后，监控器利用学习到的iHMM来推导潜在安全违规的风险估计。本文做出了三项关键贡献：（1）形式化了学习鲁棒运行时监控器的问题；（2）引入了一种新颖的框架，该框架使用基于一致性测试的细化来学习具有收敛保证的鲁棒iHMM；（3）提出了一种用于在iHMM上计算风险估计的高效监控算法。我们的实证结果表明，采用我们方法学习到的监控器具有显著效能，尤其是在与仅依赖收集数据而无系统模型访问权限的无模型监控方法进行比较时。