网络物理系统中的攻击检测与识别——第二部分：集中式与分布式监控器设计
Attack Detection and Identification in Cyber-Physical Systems -- Part II: Centralized and Distributed Monitor Design
AI总结：
本文基于第一部分结果，提出最优集中式攻击检测与识别监控器，并利用波形松弛技术设计最优分布式攻击检测滤波器，同时针对计算困难的攻击识别问题设计了具有性能保证的次优分布式识别方法。
摘要：
网络物理系统集成了计算、通信和物理能力，以与物理世界和人类交互。除了组件故障外，网络物理系统容易受到恶意攻击，因此需要开发特定的分析工具和监控机制来增强系统的安全性和可靠性。本文基于我们配套论文[1]中提出的结果，提出了用于攻击检测和识别的集中式和分布式监控器。首先，我们设计了最优的集中式攻击检测和识别监控器。最优性指的是能够检测（分别识别）每个可检测（分别可识别）的攻击。其次，我们基于波形松弛技术设计了最优的分布式攻击检测滤波器。第三，我们表明攻击识别问题在计算上是困难的，并设计了一种具有性能保证的次优分布式攻击识别程序。最后，通过仿真研究，我们展示了监控器对系统噪声和未建模动态的鲁棒性。
英文摘要：
Cyber-physical systems integrate computation, communication, and physical capabilities to interact with the physical world and humans. Besides failures of components, cyber-physical systems are prone to malicious attacks so that specific analysis tools and monitoring mechanisms need to be developed to enforce system security and reliability. This paper builds upon the results presented in our companion paper [1] and proposes centralized and distributed monitors for attack detection and identification. First, we design optimal centralized attack detection and identification monitors. Optimality refers to the ability of detecting (respectively identifying) every detectable (respectively identifiable) attack. Second, we design an optimal distributed attack detection filter based upon a waveform relaxation technique. Third, we show that the attack identification problem is computationally hard, and we design a sub-optimal distributed attack identification procedure with performance guarantees. Finally, we illustrate the robustness of our monitors to system noise and unmodeled dynamics through a simulation study.
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。
