Adversarial attacks on deep learning models in smart grids

A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid advancement of deep learning techniques, which pose...

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Autores principales:	Jingbo Hao, Yang Tao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Smart grid Data attack Adversarial example Deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/42b1c933bed14f03a943ef8c6ea56495
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id	oai:doaj.org-article:42b1c933bed14f03a943ef8c6ea56495
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spelling	oai:doaj.org-article:42b1c933bed14f03a943ef8c6ea564952021-12-04T04:34:49ZAdversarial attacks on deep learning models in smart grids2352-484710.1016/j.egyr.2021.11.026https://doaj.org/article/42b1c933bed14f03a943ef8c6ea564952022-05-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011707https://doaj.org/toc/2352-4847A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid advancement of deep learning techniques, which poses an evident threat to those deep learning models deployed in smart grids. In the face of the emergent problem, we make a compact survey of the adversarial attacks against deep learning models in smart grids. The research status of deep learning applications in smart grids and adversarial machine learning is briefly summarized firstly. Adversarial evasion and poisoning attacks in smart grids are analyzed and exemplified respectively with focus. To mitigate the threat typical countermeasures against adversarial attacks are also presented. From the survey it can be concluded that the threat of adversarial attacks in smart grids will be a kind of long-term existence and need continuous attention.Jingbo HaoYang TaoElsevierarticleSmart gridData attackAdversarial exampleDeep learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 123-129 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Smart grid Data attack Adversarial example Deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Smart grid Data attack Adversarial example Deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jingbo Hao Yang Tao Adversarial attacks on deep learning models in smart grids
description	A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid advancement of deep learning techniques, which poses an evident threat to those deep learning models deployed in smart grids. In the face of the emergent problem, we make a compact survey of the adversarial attacks against deep learning models in smart grids. The research status of deep learning applications in smart grids and adversarial machine learning is briefly summarized firstly. Adversarial evasion and poisoning attacks in smart grids are analyzed and exemplified respectively with focus. To mitigate the threat typical countermeasures against adversarial attacks are also presented. From the survey it can be concluded that the threat of adversarial attacks in smart grids will be a kind of long-term existence and need continuous attention.
format	article
author	Jingbo Hao Yang Tao
author_facet	Jingbo Hao Yang Tao
author_sort	Jingbo Hao
title	Adversarial attacks on deep learning models in smart grids
title_short	Adversarial attacks on deep learning models in smart grids
title_full	Adversarial attacks on deep learning models in smart grids
title_fullStr	Adversarial attacks on deep learning models in smart grids
title_full_unstemmed	Adversarial attacks on deep learning models in smart grids
title_sort	adversarial attacks on deep learning models in smart grids
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/42b1c933bed14f03a943ef8c6ea56495
work_keys_str_mv	AT jingbohao adversarialattacksondeeplearningmodelsinsmartgrids AT yangtao adversarialattacksondeeplearningmodelsinsmartgrids
_version_	1718372963142598656

Adversarial attacks on deep learning models in smart grids

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