Gray-box adversarial testing for control systems with machine learning components
Gray-box adversarial testing for control systems with machine learning components
复制标题
具有机器学习组件的控制系统的灰盒对抗性测试
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Georgios Fainekos
中科院分区：
文献类型：
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作者：
Shakiba Yaghoubi;Georgios Fainekos
过去已经提出了神经网络（NN），作为对具有非常复杂动力学的系统的建模和控制的有效手段。但是，尽管进行了广泛的研究，但该行业尚未采用基于NN的控制器来进行安全关键系统。主要的原因是，具有基于学习的控制器的系统很难测试和验证。针对系统级规范的此类系统的分析甚至更难。在本文中，我们提供了一种基于梯度的方法，用于搜索闭环控制系统的输入空间，以便根据某些系统级别的要求找到对抗样本。我们的实验结果表明，与随机搜索相结合，我们的方法优于模拟退火优化。
Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning based controllers are notoriously hard to test and verify. Even harder is the analysis of such systems against system-level specifications. In this paper, we provide a gradient based method for searching the input space of a closed-loop control system in order to find adversarial samples against some system-level requirements. Our experimental results show that combined with randomized search, our method outperforms Simulated Annealing optimization.