基于边缘计算的电网假数据攻击分布式检测方法

虚假数据注入攻击(FDIA)作为新型的电网攻击手段,严重威胁智能电网的安全运行。爆炸式增长的数据给集中式的FDIA检测方法带来了巨大的挑战。基于此,提出了一种基于边缘计算的分布式检测方法。将系统拆分为多个子系统,且在子系统中设置边缘节点检测器进行数据的收集、检测。结合深度学习的方法,构建了CNN-LSTM模型检测器,提取数据特征,并将模型的训练过程放置在中心节点上,实现高效、低时延的FDIA检测。最后在IEEE14节点和IEEE39节点测试系统中,设定不同攻击强度,对所提边缘检测方法进行验证。结果表明,与集中式的检测方法相比,所提边缘检测方法在检测时间和内存消耗两个指标上有明显的下降。

Distributed detection method for a false data attack in a power grid based on edge computing

A new method of power grid attack, the False Data Injection Attack (FDIA), seriously threatens the safe operation of smart grids. The explosive growth of data has brought huge challenges to centralized FDIA detection methods. This paper proposes a detection method based on edge computing, which divides the system into multiple subsystems, and sets edge node detectors in the subsystems for data collection and detection. Combined with deep learning methods, a CNN-LSTM detecting model is constructed to extract the characteristics of the data, and the training process of the model is placed on the central node to achieve efficient and low-latency FDIA detection. Finally, the proposed edge detection method is verified in the IEEE 14-node and IEEE 39-node test systems for different attack intensities. Compared with the centralized detection method, the results show that the advanced edge detection method can achieve a significant drop in detection time and memory consumption. This work is supported by the National Natural Science Foundation of China (No. 41671431).