基于通道融合的Res-CNN-LSTM电网虚假数据注入攻击检测OA
Detection of false data injection attacks in power grid based on Res-CNN-LSTM with channel fusion
针对电力系统的网络攻击事件越来越多,信息物理安全问题已经引发电力公司和学术界的高度关注.为了能够正确检测电网虚假数据注入攻击,本文提出一种基于残差神经网络(ResNet)结构的一维卷积神经网络(1DCNN)和长短期记忆(LSTM)网络多通道融合网络模型,简称通道融合的Res-CNN-LSTM网络模型.该神经网络算法利用 1DCNN和LSTM对时间序列信息的高效提取能力,将不同通道上提取的信息进行融合,进一步加强了数据特征的提取效果,同时网络模型主体采用残差跳跃连接的结构来解决神经网络在训练过程中的过拟合问题;在IEEE-14 和IEEE-118 节点测试系统进行模型仿真实验,并对比其他神经网络模型,结果验证了本文所提方法的有效性和准确性.
The number of network attacks targeting the power system is increasing,and information physical security issues have attracted high attention from power companies and academia.In order to accurately detect false data injection attacks in the power grid,a one-dimensional convolutional neural network(1DCNN)based on residual neural network(ResNet)structure,and long short-term memory(LSTM)network based multi-channel fusion network model which called Res-CNN-LSTM is proposed.This algorithm utilizes the efficient extraction ability of 1DCNN and LSTM in time series information,and fuses the extracted information in different channels to further enhance the extraction effect of data features.At the same time,the main body of the model adopts a residual jump connection structure to solve the problem of overfitting in the training process of the neural network.Simulation is conducted based on IEEE-14 and IEEE-118 node testing systems,and the proposed method is compared with other neural network model algorithms.The results verified the effectiveness and accuracy of the proposed method in the paper.
方正刚
福州大学电气工程与自动化学院,福州 350108
神经网络多通道数据融合攻击检测深度学习长短期记忆(LSTM)神经网络
neural networkmulti channel data fusionattack detectiondeep learninglong short-term memory(LSTM)neural network
《电气技术》 2024 (003)
11-17,62 / 8
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