针对控制系统的恶意传感器识别的移动目标方法
A Moving Target Approach for Identifying Malicious Sensors in Control Systems
AI总结：
本文提出一种移动目标方法，通过动态改变系统动态来主动识别恶意传感器，提升系统抗攻击能力。
摘要：
本文考虑了在物理-信息系统（CPS）中攻击识别的问题。攻击识别对于恢复和性能至关重要，允许防御者构建绕过有害节点的算法。以往的工作指出了在确定性LTI系统上完美识别对抗攻击的局限性。例如，系统在移除任何2q传感器后仍需保持可观测性才能仅识别q次攻击。然而，攻击者能够创建不可识别攻击的能力需要系统模型的知识。本文旨在通过限制攻击者的系统模型知识来准确识别所有传感器攻击。此类方案将允许系统承受更大的攻击或系统操作员在保持安全的前提下减少用于控制系统的传感设备数量。我们探讨了如何通过时间函数改变系统动态来主动识别控制系统的恶意/故障传感器，并讨论了设计时间变化系统矩阵以实现这一目标，并在确定性和随机性系统中评估性能。
英文摘要：
In this paper, we consider the problem of attack identification in cyber-physical systems (CPS). Attack identification is often critical for the recovery and performance of a CPS that is targeted by malicious entities, allowing defenders to construct algorithms which bypass harmful nodes. Previous work has characterized limitations in the perfect identification of adversarial attacks on deterministic LTI systems. For instance, a system must remain observable after removing any 2q sensors to only identify q attacks. However, the ability for an attacker to create an unidentifiable attack requires knowledge of the system model. In this paper, we aim to limit the adversary's knowledge of the system model with the goal of accurately identifying all sensor attacks. Such a scheme will allow systems to withstand larger attacks or system operators to allocate fewer sensing devices to a control system while maintaining security. We explore how changing the dynamics of the system as a function of time allows us to actively identify malicious/faulty sensors in a control system. We discuss the design of time varying system matrices to meet this goal and evaluate performance in deterministic and stochastic systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
