Spatial-Temporal Attention Based Interpretable Deep Framework for FDIA Detection in Smart Grid-Reference-Cited by-同舟云学术

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Spatial-Temporal Attention Based Interpretable Deep Framework for FDIA Detection in Smart Grid

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Sustainable Power and Energy Conference (iSPEC)
language:
Short-container-title:

Author:

Zhang Wangjun¹,Deng Chao²,Su Xiangjing²,Nie Liangzhao²,Wu Yi³

Affiliation:

1. State Grid Shanghai Municipal Electric Power Company,Internet Department,Shanghai,China

2. Shanghai University of Electric Power,College of Electrical Engineering,Shanghai,China

3. Shanghai Electric Power Research Institute,Information Security Technology Supervision Department,Shanghai,China

Funder

Science and Technology Project of State Grid

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10032972/10032974/10032978.pdf?arnumber=10032978

Reference15 articles.

1. Vulnerabilities of Smart Grid State Estimation Against False Data Injection Attack

2. Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

3. Effective Approaches to Attention-based Neural Machine Translation;m-t;ArXiv150804025 Cs,2015

4. Long Short-Term Memory

5. Blind False Data Injection Attack Using PCA Approximation Method in Smart Grid

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