论文标题
使用深度学习鉴定和纠正虚假数据注射攻击针对AC状态估计的攻击
Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning
论文作者
论文摘要
最近的文献提出了针对FDIA的各种检测和鉴定方法，但是很少有研究集中在解决此类攻击发生的解决方案上。但是，使用深度学习取得了长足的进步来检测攻击。受这些进步的启发，我们开发了一种新的方法，不仅可以识别AC FDIA，而且更重要的是，也可以进行更正。我们的方法论利用长短的术语内存denoising自动编码器（LSTM-DAE）根据攻击的测量值纠正受攻击的状态。使用IEEE 30系统评估了该方法，实验表明该方法成功地识别了损坏的状态并以很高的精度纠正它们。
recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to detect attacks. Inspired by these advancements, we have developed a new methodology for not only identifying AC FDIAs but, more importantly, for correction as well. Our methodology utilizes a Long-Short Term Memory Denoising Autoencoder (LSTM-DAE) to correct attacked-estimated states based on the attacked measurements. The method was evaluated using the IEEE 30 system, and the experiments demonstrated that the proposed method was successfully able to identify the corrupted states and correct them with high accuracy.
