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Article: Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks

TitleOnline False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks
Authors
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424
Citation
IEEE Transactions on Industrial Informatics, 2018, v. 14 n. 7, p. 3271-3280 How to Cite?
AbstractState estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.
Persistent Identifierhttp://hdl.handle.net/10722/259309
ISSN
2017 Impact Factor: 5.43
2015 SCImago Journal Rankings: 2.973
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorHou, Y-
dc.contributor.authorLi, VOK-
dc.date.accessioned2018-09-03T04:04:55Z-
dc.date.available2018-09-03T04:04:55Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Industrial Informatics, 2018, v. 14 n. 7, p. 3271-3280-
dc.identifier.issn1551-3203-
dc.identifier.urihttp://hdl.handle.net/10722/259309-
dc.description.abstractState estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424-
dc.relation.ispartofIEEE Transactions on Industrial Informatics-
dc.rightsIEEE Transactions on Industrial Informatics. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. -
dc.titleOnline False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks-
dc.typeArticle-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailHou, Y: yhhou@hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityHou, Y=rp00069-
dc.identifier.authorityLi, VOK=rp00150-
dc.identifier.doi10.1109/TII.2018.2825243-
dc.identifier.hkuros289636-
dc.identifier.volume14-
dc.identifier.issue7-
dc.identifier.spage3271-
dc.identifier.epage3280-
dc.identifier.isiWOS:000437883400046-
dc.publisher.placeUSA-

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