We introduce the problem of learning-based attacks in a simple abstraction of cyber-physical systems— the case of a discrete-time, linear, time-invariant plant that may be subject to an attack that overrides sensor readings and controller actions. The attacker attempts to learn the dynamics of the plant and subsequently overrides the controller's actuation signal to destroy the plant without being detected. The attacker can feed fictitious sensor readings to the controller using its estimate of the plant dynamics and mimic the legitimate plant operation. The controller, in contrast, is constantly on the lookout for an attack; once the controller detects an attack, it immediately shuts the plant off. In the case of scalar plants, we derive an upper bound on the attacker's deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics. We then derive lower bounds for the attacker's deception probability for both scalar and vector plants by assuming an authentication test that inspects the empirical variance of the system disturbance. We also show how the controller can improve the security of the system by superimposing a carefully crafted privacy-enhancing signal on top of the “nominal control policy.” Finally, for nonlinear scalar dynamics that belong to the reproducing kernel Hilbert space, we investigate the performance of attacks based on nonlinear Gaussian process learning algorithms.

中文翻译：

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网络物理系统中基于学习的攻击

我们在网络物理系统的简单抽象中引入了基于学习的攻击的问题，即离散时间，线性，时不变的工厂，这种情况可能会受到覆盖传感器读数和控制器动作的攻击。攻击者尝试了解植物的动态，随后覆盖控制器的驱动信号以破坏植物而不被发现。攻击者可以使用对工厂动态的估计，将虚拟传感器的读数提供给控制器，并模仿合法的工厂操作。相反，控制器一直在寻找攻击。一旦控制器检测到攻击，它将立即关闭工厂。对于标量植物，我们得出攻击者的上限 攻击者使用任意学习算法估算系统动态时，对于任何可衡量的控制策略的欺骗概率。然后，我们通过假设验证测试来检查系统扰动的经验方差，从而得出针对标量植物和矢量植物的攻击者欺骗概率的下限。我们还将展示控制器如何通过在“名义控制策略”之上叠加精心设计的隐私增强信号来提高系统的安全性。最后，对于属于可再生内核Hilbert空间的非线性标量动力学，我们研究了基于非线性高斯过程学习算法的攻击性能。标量和矢量植物的s欺骗概率，方法是进行验证检验，该检验检查系统扰动的经验方差。我们还将展示控制器如何通过在“名义控制策略”之上叠加精心设计的隐私增强信号来提高系统的安全性。最后，对于属于可再生内核Hilbert空间的非线性标量动力学，我们研究了基于非线性高斯过程学习算法的攻击性能。标量和矢量植物的s欺骗概率，方法是假设验证测试检查系统扰动的经验方差。我们还将展示控制器如何通过在“名义控制策略”之上叠加精心设计的隐私增强信号来提高系统的安全性。最后，对于属于可再生内核Hilbert空间的非线性标量动力学，我们研究了基于非线性高斯过程学习算法的攻击性能。