Simultaneous Detection and Estimation of False Data Injection Attacks in Cyber-Physical Battery Systems using a Learning Observer
Simultaneous Detection and Estimation of False Data Injection Attacks in Cyber-Physical Battery Systems using a Learning Observer
复制标题
DOI：
10.1109/iccad57653.2023.10152377
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Wen Chen;F. Lin;L. Wang
中科院分区：
文献类型：
--
作者：
Wen Chen;F. Lin;L. Wang
本文提出了一种基于学习的方法来同时检测和估计对网络物理电池系统的虚假数据注入攻击。在状态空间配方的原始电池系统被转换成两个独立的子系统：一个包含干扰和FDIA和第二个是免费的干扰，但受FDIA。然后为第二个子系统设计一个学习观测器，使得FDIA信号可以在不受干扰影响的情况下被估计和进一步检测。这使得所提出的基于学习模糊器的检测和估计方法对干扰具有鲁棒性，并且可以避免FDIA的误报。该方法的另一个优点是由于设计了降维学习观测器，计算量小。以一个三节电池的电池组为例，仿真研究验证了所提出的FDIA检测和估计方法的有效性。
This work is to present a learning observer-based method for simultaneous detection and estimation of false data injection attacks (FDIAs) to the cyber-physical battery systems. The original battery system in a state-space formulation is transformed into two separate subsystems: one contains both disturbances and the FDIAs and the second one is free from disturbances but subject to FDIAs. A learning observer is then designed for the second subsystem such that the FDIA signals can be estimated and further detected without being affected by the disturbances. This makes the proposed learning observer-based detection and estimation method is robust to disturbances and false declaration of FDIAs can be avoided. Another advantage of the proposed method is that the computing load is low because of the design of a reduced-order learning observer. With a three-cell battery string, a simulation study is employed to verify the effectiveness of proposed detection and estimation method for the FDIAs.