Monitoring the correctness of distributed cyber-physical systems is essential. Detecting possible safety violations can be hard when some samples are uncertain or missing. We monitor here black-box cyber-physical system, with logs being uncertain both in the state and timestamp dimensions: that is, not only the logged value is known with some uncertainty, but the time at which the log was made is uncertain too. In addition, we make use of an over-approximated yet expressive model, given by a non-linear extension of dynamical systems. Given an offline log, our approach is able to monitor the log against safety specifications with a limited number of false alarms. As a second contribution, we show that our approach can be used online to minimize the number of sample triggers, with the aim at energetic efficiency. We apply our approach to three benchmarks, an anesthesia model, an adaptive cruise controller and an aircraft orbiting system.

翻译：监测分布式信息物理系统的正确性至关重要。当部分样本存在不确定性或缺失时，检测可能的安全违规行为将变得困难。本文针对黑盒信息物理系统进行监测，其日志在状态和时间戳两个维度均存在不确定性：不仅记录值具有某种不确定性，日志生成的时间也是不确定的。此外，我们采用一种过近似但表达能力强的模型，该模型由动力系统的非线性扩展构成。对于离线日志，我们的方法能够以有限的误报率对日志进行安全规范监测。作为第二项贡献，我们展示了该方法可在线使用，通过最小化样本触发次数实现能效优化。我们将该方法应用于三个基准测试：麻醉模型、自适应巡航控制器和飞机轨道系统。