一句话：提出基于非线性动力系统过近似边界模型的日志监控方法，支持离线安全违例检测与在线节能采样。
分布式信息物理系统的正确性监控至关重要，但部分样本不确定或缺失时，检测潜在安全违例十分困难。本文针对黑盒系统，处理状态与时间戳均不确定的日志：记录值和记录时间都带有不确定性。方法采用动力系统非线性扩展给出的过近似、表达力较强的边界模型，将离线日志与安全规约比对，在有限误报下监控违例。第二项贡献表明，该方法也可在线运行，通过减少采样触发次数提升能效。实验在麻醉模型、自适应巡航控制器和飞机环绕系统三个基准上评估。
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.
@article{paperbot2788,
title = {Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model},
author = {Bineet Ghosh and Étienne André},
journal = {Logical Methods in Computer Science},
volume = {Volume 20, Issue 1},
year = {2024},
doi = {10.46298/lmcs-20(1:2)2024}
}