Document Type
Conference Proceeding
Publication Date
12-2024
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Source Publication
GLOBECOM 2024 - 2024 IEEE Global Communications Conference
Source ISSN
2576-6813
Abstract
Fault detection is an integral part of the protection system in a power distribution network. Due to advanced computational capabilities, deep learning-based algorithms can significantly outperform traditional methods. However, these deep learning models are prone to adversarial attacks which are not well-addressed as traditional cyber attacks in distribution systems. More specifically, to capture the structure of distribution systems, graph neural networks (GNNs) are employed. Leveraging the backdoor attack model, we propose a novel graph-based adversarial attack algorithm for fault detection in power systems. It is further shown that the adaptable structure of GNN can make them vulnerable to adversarial attacks through graph motif insertion. The backdoor attack is carried out on IEEE-13 and IEEE-37 feeder systems and numerical results demonstrate a substantial decrease in fault detection accuracy as a result of these attacks.
Recommended Citation
Chanda, Dibaloke and Yahyasoltani, Nasim, "A Graph Motif Adversarial Attack for Fault Detection in Power Distribution Systems" (2024). Computer Science Faculty Research and Publications. 112.
https://epublications.marquette.edu/comp_fac/112
Comments
Accepted version. GLOBECOM 2024 : IEEE Global Communications Conference, (2025): DOI. © 2024 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.