虚假数据注入攻击(false data injection attack,FDIA)是智能电网安全与稳定运行面临的严重威胁.文中针对FDIA检测中存在的有标签数据稀少、正常和攻击样本极不平衡的问题,提出了融合无监督和有监督学习的FDIA检测算法.首先引入对比学习捕获少量攻击数据特征,生成新的攻击样本实现数据扩充;然后利用多种无监督检测算法对海量的无标签样本进行特征自学习,解决有标签样本稀缺的问题;最后将无监督算法提取的特征与历史特征集进行融合,在新的特征空间上构建有监督XGBoost分类器进行识别,输出正常或异常的检测结果.在IEEE 30 节点系统上的算例分析表明,与其他FDIA检测算法相比,文中方法增强了 FDIA 检测模型在有标签样本稀少和数据不平衡情况下的稳定性,提升了FDIA的识别精度并降低了误报率.
Detection method of false data injection attack based on unsupervised and supervised learning
False data injection attack(FDIA)is a serious threat to the security and stable operation of smart grids.In this paper,a FDIA detection algorithm that combines unsupervised and supervised learning is proposed,solving the problems of scarce labeled data and extremely imbalanced normal and attack samples.Firstly,contrastive learning is introduced to capture the features of a small amount of attack data,and it generates new attack samples to achieve data augmentation.Then,various unsupervised detection algorithms are used to perform feature self-learning on a large number of unlabeled samples,addressing the problem of scarce labeled samples.Finally,the features extracted by the unsupervised algorithm are fused with the historical feature set,and a supervised XGBoost classifier is constructed to identify and output the detection results.The results on the IEEE 30-node system show that the proposed method can enhance the stability of the FDIA detection model under scarce labeled samples and imbalanced data,compared with other FDIA detection algorithms.The proposed method can improve recognition accuracy and reduce false alarm rate.
false data injection attack(FDIA)supervised learningunsupervised learningcontrastive learningdata expansionfeature enhancement
万方数据
维普
