发表机构
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对存在未知有界噪声的传感器融合网络物理系统，提出基于 zonotope 的主动暴露方法，通过设计滚动时域优化框架的暴露输入实现隐蔽欺骗攻击检测，经无人机导航仿真验证了方法有效性。
AI 中文摘要
本文研究了存在未知有界噪声的传感器融合网络物理系统通过控制信道的隐蔽攻击检测问题，该检测框架特别适用于多个对融合估计有贡献的可疑传感器可能同时被入侵的传感器融合场景。首先，利用安全传感器构造可允许输出集，并针对每个攻击假设构造攻击输出集。随后，引入滚动时域优化框架来设计暴露输入，即通过控制信道注入的有界辅助控制扰动，以根据分离趋势扩大可允许输出集与攻击输出集之间的分离度。进一步推导了充分检测条件，表明集分离可保证被入侵传感器的可检测性。此外，开发了离线暴露预算指导，以支持在线暴露开始前的预算选择。对遭受隐蔽 GNSS 和 LiDAR 攻击的无人机导航系统的仿真验证了所提方法的有效性。
英文摘要
This paper investigates the stealthy attack detection for sensor-fusion cyber-physical systems with unknown-but-bounded noises through the control channel. The detection framework is particularly applicable to sensor-fusion scenarios in which multiple suspicious sensors contributing to the fused estimate may be compromised simultaneously. First, we construct an admissible output set using secure sensors and an attack output set for each attack hypothesis. Then, we introduce a receding-horizon optimization framework to design exposure inputs, namely bounded auxiliary control perturbations injected through the control channel, so as to enlarge the separation between the admissible output set and the attack output sets according to the separation tendency. A sufficient detection condition is further derived, showing that set separation guarantees detectability of the compromised sensors. Moreover, an offline exposure budget guidance is developed to support budget selection before online exposure starts. Simulations on a UAV navigation system under stealthy GNSS and LiDAR attacks validate the proposed method.
