发表机构
Center for Automotive Research, The Ohio State University（俄亥俄州立大学汽车研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出在资源与白噪声约束下，通过记忆修正创新能量并采用迟滞阈值规则，实现隐蔽攻击调度，在保持高损害的同时显著降低白噪声暴露。
AI 中文摘要
我们研究了在资源约束下对传感器到估计器链路的隐蔽攻击调度问题，该约束包括对损坏传输比例的预算\bar{\Gamma}以及对接收创新序列的无模型白噪声约束。对于具有非高斯噪声的离散时间线性系统，最严重的攻击——创新符号翻转——保持了创新幅度，并且对每个幅度可测量的检测器都是完全隐蔽的，而损害最优的调度是无记忆阈值；但最大化损害的尾部集中也制造了序列相关性，此时创新白噪声监测器获得检测能力。我们对白噪声约束进行对偶化，并证明最优策略是基于修正创新能量的阈值规则，该规则利用近期幅度和先前决策的记忆。我们进一步将该修正追溯为第二个自由度，并通过幅度空间中触发集的位置来设定损害，通过其运行结构的边界密度来设定暴露程度。我们将其实现为通过一个分裂保形顺序统计量设定的迟滞集，无需任何系统模型，每步仅使用一个计数器和两次比较。在真实卡车CAN记录上验证，该方法在2.3到71倍更低的滞后一阶白噪声功率下，保持了无记忆损害的80%至96%。
英文摘要
We pose stealthy attack scheduling on a sensor-to-estimator link under a resource constraint with a budget \barΓ on the fraction of corrupted transmissions and a model-free whiteness constraint on the received innovations. For a discrete-time linear plant with non-Gaussian noise, the worst attack, innovation sign flip, preserves the innovation magnitude and is exactly stealthy against every magnitude-measurable detector, the damage-optimal schedule being a memoryless threshold; but the tail concentration that maximizes damage also manufactures serial correlation, where an innovation-whiteness monitor gains power. We dualize the whiteness constraint and show the optimum is a threshold rule on corrected innovation energy using memory of recent magnitudes and the previous decision. We further trace that correction to a second degree of freedom and set the damage by the location of the firing set in magnitude space and set exposure by the boundary density of its run structure. We realize it as a hysteresis set by one split-conformal order statistic without any plant model, using one counter and two comparisons per step. It keeps 80--96\% of the memoryless damage at 2.3 to 71 times lower lag-one whiteness power, validated on a real truck CAN record.
