建筑物中的数据驱动控制和数据中毒攻击：KTH室内实验室案例研究
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study
摘要：
本工作探讨了使用输入-输出数据驱动的控制技术进行建筑控制的可行性及其对数据中毒技术的敏感性。分析是在KTH Livein实验室的数字复制品上进行的，这是一个非线性验证模型，代表了KTH Livein实验室建筑测试平台之一。这项工作的动机是最近的趋势表明，使用基于数据的技术来控制网络物理系统的兴趣激增。我们还分析了这些控制器对数据中毒方法的敏感性，这是一种特殊类型的机器学习威胁，旨在发现可能破坏所考虑系统性能的不可察觉的攻击。我们认为虚拟参考反馈调谐（VRFT），一个流行的数据驱动的控制技术，并显示其性能的KTH生活在实验室数字副本。然后，我们将展示如何制作中毒攻击，并说明这种攻击的影响。数值实验表明，使用数据驱动的控制方法来寻找有效的控制律的可行性。然而，数据集的细微变化会显着降低VRFT的性能。
英文摘要：
This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques. The analysis is performed on a digital replica of the KTH Livein Lab, a non-linear validated model representing one of the KTH Live-in Lab building testbeds. This work is motivated by recent trends showing a surge of interest in using data-based techniques to control cyber-physical systems. We also analyze the susceptibility of these controllers to data-poisoning methods, a particular type of machine learning threat geared towards finding imperceptible attacks that can undermine the performance of the system under consideration. We consider the Virtual Reference Feedback Tuning (VRFT), a popular data-driven control technique, and show its performance on the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks can be crafted and illustrate the impact of such attacks. Numerical experiments reveal the feasibility of using data-driven control methods for finding efficient control laws. However, a subtle change in the datasets can significantly deteriorate the performance of VRFT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
