Covariance-Robust Dynamic Watermarking
Covariance-Robust Dynamic Watermarking
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DOI：
10.1109/cdc42340.2020.9304039
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Matt Olfat;S. Sloan;P. Hespanhol;Matthew Porter;Ram Vasudevan;A. Aswani
中科院分区：
文献类型：
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作者：
Matt Olfat;S. Sloan;P. Hespanhol;Matthew Porter;Ram Vasudevan;A. Aswani
网络物理系统（CPS）的攻击检测和缓解策略是一个活跃的研究领域，研究人员开发了各种攻击检测工具，例如动态水印。然而，此类方法常常做出难以保证的假设，例如准确了解测量噪声的分布。在这里，我们开发了一种新的动态水印方法，称为协方差鲁棒动态水印，它能够处理测量噪声协方差中的不确定性。具体来说，我们考虑两种情况。在第一个中，该协方差是固定但未知的，而在第二个中，该协方差是缓慢变化的。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兴领域联系起来，并且我们表明我们的测试满足一些公平性的概念。最后，我们展示了对反映自动驾驶汽车标准模拟模型中观察到的值的经验示例进行测试的有效性。
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.