A multi-module robust method for transient stability assessment against false label injection cyberattacks

Hanxuan Wang, Na Lu, Yinhong Liu, Zhuqing Wang and Zixuan Wang

Applied Energy, 2025, vol. 389, issue C, No S0306261925004982

Abstract: The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label injection (FLI) cyberattacks, resulting in degraded performance of deep TSA models. To address this challenge, a Multi-Module Robust (MMR) TSA method is proposed to rectify the supervised training process misguided by FLI attacks in an unsupervised manner. In MMR, a supervised classification module and an unsupervised clustering module are alternately trained to improve the clustering friendliness of representation leaning, achieving accurate clustering assignments. By leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and correct the misguided supervised classification, thereby improving the performance of deep TSA models. However, there is still a gap on accuracy and convergence speed between MMR and FLI-free deep TSA models. To narrow this gap, we further propose a human-in-the-loop training strategy, named MMR-HIL. In MMR-HIL, potential false samples can be detected by modeling the training loss with a Gaussian distribution. From these samples, the most likely false samples and most ambiguous samples are selected and re-labeled by a TSA expert-guided annotator and then subjected to penalized optimization, aimed at improving accuracy and convergence speed. Extensive experiments indicate that MMR and MMR-HIL both exhibit powerful robustness against FLI attacks. Moreover, the contaminated labels can be effectively corrected, demonstrating superior resilience of the proposed methods.

Keywords: False label injection; Human-in-the-loop; Multi-module; Robustness; Transient stability assessment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925004982
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:389:y:2025:i:c:s0306261925004982

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125768

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().