南方电网技术2025，Vol.19Issue(1)：74-84,11.DOI:10.13648/j.cnki.issn1674-0629.2025.01.008
基于改进卷积神经网络的电网虚假数据注入攻击定位方法
Improved Convolutional Neural Network-Based Localization Method for False Data Injection Attacks on Power Grids
摘要
Abstract
False data injection attacks disrupt the stability of power systems by tampering with the data collected by data acquisition and monitoring control systems.Traditional methods for detecting false data injection attacks are unable to locate the attacked loca-tion or have low accuracy.Firstly,an improved method for detecting false data injection attacks using seagull optimized convolutional neural networks is proposed.The proposed method uses a convolutional neural network with shared weights and local connectivity to efficiently extract and classify features from high-dimensional historical measurement data.Secondly,an improved seagull optimization algorithm with balanced global and local search capabilities is introduced to perform hyperparametric optimiza-tion to obtain a highly matched network structure for false data detection.The network structure is then used to detect and locate bad data.Finally,the effectiveness of the proposed method is verified through extensive attack detection experiments on IEEE-14 and IEEE-57 node systems,and compared with various other detection methods to verify that the proposed method has better classifica-tion performance,higher accuracy,precision,recall,and F1 value.关键词
虚假数据注入攻击/电力系统/卷积神经网络/海鸥优化/数据检测Key words
false data injection attacks/power systems/convolutional neural networks/seagull optimization/data detection分类
动力与电气工程引用本文复制引用
席磊,程琛,田习龙..基于改进卷积神经网络的电网虚假数据注入攻击定位方法[J].南方电网技术,2025,19(1):74-84,11.基金项目
国家自然科学基金资助项目(52277108,S2477104).Supported by the National Natural Science Foundation of China(52277108,52477104). （52277108,S2477104）
