南方电网技术2024，Vol.18Issue(5)：39-50,61,13.DOI:10.13648/j.cnki.issn1674-0629.2024.05.005
基于相关特征-多标签级联提升森林的电网虚假数据注入攻击定位检测
Locational Detection of False Data Injection Attack in Power Grid Based on Relevant Features Multi-Label Cascade Boosting Forest
摘要
Abstract
False data injection attack seriously endanger the safety and stability of the power grid operations.Due to the high dimen-sion and complex characteristics of the electricity measurement data,the attack locational detection accuracies of the existing methods are insufficient.For this reason,a false data injection attack locational detection method based on relevant features multi-label cascade boosting forest is proposed to locate the attacked position of the power grid.The proposed method enhances the fitting ability of the multi-label cascade forest processing the complex electricity measurement data by incorporating the extreme gradient boosting algorithm,so as to identify the abnormal state variables of each bus.Furthermore,the"relevant features"algorithm is integrated to extract the highly informative features from the original electricity measurement data to improve the generalization ability of the multi-label cascade forest,so as to obtain more accurate location detection.The simulation results on IEEE 14-bus and IEEE 57-bus test systems verify the effectiveness of the proposed method,and compared with many other methods,the proposed method has better ac-curacy,precision,sensitivity and F1-score.关键词
虚假数据注入攻击/相关特征/多标签级联森林/极端梯度提升Key words
false data injection attack/relevant features/multi-label cascade forest/extreme gradient boosting分类
动力与电气工程引用本文复制引用
席磊,田习龙,余涛,程琛..基于相关特征-多标签级联提升森林的电网虚假数据注入攻击定位检测[J].南方电网技术,2024,18(5):39-50,61,13.基金项目
国家自然科学基金资助项目(52277108). Supported by the National Natural Science Foundation of China(52277108). （52277108）
