发表机构
Center for Automotive Research, The Ohio State University（汽车研究中心，俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出免分布预算隐蔽FDI调度器，结合最坏情况行动与分裂共形校准触发器，实现路径级隐蔽性和预算保证，无需分布假设，并通过实验验证其有效性。
AI 中文摘要
本文针对远程状态估计中的预算受限隐蔽虚假数据注入（FDI）调度问题，其中资源受限的对手最多可破坏比例为$\bar{\Gamma}$的传输。现有的事件触发调度器通过反转高斯创新尾部来设置触发阈值，并通过协方差匹配来证明隐蔽性；这两种方法仅在高斯性假设下精确成立，而实际网络物理残差通常违反该假设。我们提出了一种免分布的调度器，将最坏情况FDI行动与分裂共形校准触发器相结合，既不需要系统矩阵，也不需要任何分布模型。我们建立了针对每个幅度可测检测器的精确路径级隐蔽性，适用于任意触发规则和创新分布；给出了平均触发率的有限样本免分布界，在平稳性和遍历性下几乎必然达到预算；以及一个关于单一标量能量捕获$\psi$的线性稳态退化恒等式，该恒等式由提供预算保证的同一阶统计量最大化。一个条件符号对称性条件界定了证书何时扩展到符号敏感检测器，残余暴露由在高斯噪声下消失的四阶累积量控制。蒙特卡洛研究和重型卡车CAN记录证实了这些界，并量化了高斯假设在其适用范围之外的成本。
英文摘要
This letter addresses budgeted stealthy false-data-injection (FDI) scheduling against remote state estimation, where a resource-constrained adversary may corrupt at most a fraction $\barΓ$ of transmissions. Existing event-triggered schedulers invert a Gaussian innovation tail to set the firing threshold and certify stealth by covariance matching; both are exact only under Gaussianity, which real cyber-physical residuals routinely violate. We propose a distribution-free scheduler pairing the worst-case FDI action with a split-conformal calibrated trigger, requiring neither the plant matrices nor any distributional model. We establish exact pathwise stealth against every magnitude-measurable detector, for any firing rule and innovation law; a finite-sample distribution-free bound on the mean firing rate, with almost-sure budget attainment under stationarity and ergodicity; and a steady-state degradation identity linear in a single scalar energy capture $ψ$, maximized by the same order statistic that delivers the budget guarantee. A conditional sign-symmetry condition delimits when the certificate extends to sign-sensitive detectors, the residual exposure being governed by a fourth cumulant that vanishes under Gaussian noise. Monte-Carlo studies and a heavy-duty truck CAN record confirm the bounds and quantify what the Gaussian assumption costs outside its regime.
