论文标题
协方差动态水印
Covariance-Robust Dynamic Watermarking
论文作者
论文摘要
网络物理系统（CPS）的攻击检测和缓解策略是一个积极的研究领域，研究人员开发了各种攻击检测工具，例如动态水印。但是，这种方法通常会做出难以保证的假设，例如确切的测量噪声分布的知识。在这里，我们开发了一种新的动态水印方法，我们称之为协方差动态水印，该方法能够在测量噪声的协方差中处理不确定性。具体来说，我们考虑两种情况。第一个协方差是固定的，但未知，第二个协方差正在缓慢变化。对于我们的测试，我们只需要了解协方差所在的集合。此外，我们将这个问题与算法公平性和公平假设检验的新生领域联系起来，我们表明我们的测试满足了一些公平的概念。最后，我们在选择以反映在自动驾驶的标准仿真模型中观察到的经验实例的测试功效。
Attack detection and mitigation strategies for cyberphysical systems (CPS) are an active area of research, and researchers have developed a variety of attack-detection tools such as dynamic watermarking. However, such methods often make assumptions that are difficult to guarantee, such as exact knowledge of the distribution of measurement noise. Here, we develop a new dynamic watermarking method that we call covariance-robust dynamic watermarking, which is able to handle uncertainties in the covariance of measurement noise. Specifically, we consider two cases. In the first this covariance is fixed but unknown, and in the second this covariance is slowly-varying. For our tests, we only require knowledge of a set within which the covariance lies. Furthermore, we connect this problem to that of algorithmic fairness and the nascent field of fair hypothesis testing, and we show that our tests satisfy some notions of fairness. Finally, we exhibit the efficacy of our tests on empirical examples chosen to reflect values observed in a standard simulation model of autonomous vehicles.
