Research Article
Semi-supervised False Data Injection Attacks Detection in Smart Grid
@INPROCEEDINGS{10.1007/978-3-031-17081-2_12, author={Yasheng Zhou and Li Yang and Yang Cao}, title={Semi-supervised False Data Injection Attacks Detection in Smart Grid}, proceedings={Applied Cryptography in Computer and Communications. Second EAI International Conference, AC3 2022, Virtual Event, May 14-15, 2022, Proceedings}, proceedings_a={AC3}, year={2022}, month={10}, keywords={False data injection attacks Semi-supervised learning Label propagation Small sample learning}, doi={10.1007/978-3-031-17081-2_12} }- Yasheng Zhou
Li Yang
Yang Cao
Year: 2022
Semi-supervised False Data Injection Attacks Detection in Smart Grid
AC3
Springer
DOI: 10.1007/978-3-031-17081-2_12
Abstract
False data injection attacks (FDIAs) detection in smart grid, requires adequate labeled training samples to train a detection model. Due to the strong subjectivity, relying on expert knowledge and time-consuming nature of power system sample annotation, this task is intrinsically a small sample learning problem. In this paper, we propose a novel semi-supervised detection algorithm for FDIAs detection. The semi-supervised label propagation algorithm can dynamically propagate the label from labeled samples to unlabeled samples, automatically assign class labels to the unlabeled samples dataset, and enlarge the labeled samples dataset. Jointly use a small number of manually labeled samples dataset and a large number of auto-labeled samples dataset to construct a classifier via semi-supervised learning. Comparing the proposed algorithm with supervised learning algorithms, the results suggest that, with the scheme of semi-supervised learning from large unlabeled dataset, the proposed algorithm can significantly improve the accuracy of false data injection attacks detection.
