AI based approach to identify compromised meters in data integrity attacks on smart grid

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dc.contributor.authorKhanna, Kush
dc.contributor.authorPanigrahi, Bijaya Ketan
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2018-10-16T13:33:15Z
dc.date.available2018-10-16T13:33:15Z
dc.date.issued2017-10-02
dc.description.abstractFalse data injection attacks can pose serious threats to the operation and control of power grid. The smarter the power grid gets, the more vulnerable it becomes to cyber attacks. Various detection methods of cyber attacks have been proposed in the literature in recent past. However, to completely alleviate the possibility of cyber threats, the compromised meters must be identified and secured. In this paper, we are presenting an Artificial Intelligence (AI) based identification method to correctly single out the malicious meters. The proposed AI based method successfully identifies the compromised meters by anticipating the correct measurements in the event of the cyber attack. NYISO load data is mapped with the IEEE 14 bus system to validate the proposed method. The efficiency of the proposed method is compared for Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) based AI techniques. It is observed that both the techniques identify the corrupted meters with high accuracy.en_US
dc.description.urihttp://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.0455en_US
dc.format.extent23 pagesen_US
dc.genrejournal article post-printen_US
dc.identifierdoi:10.13016/M23T9D984
dc.identifier.citationKush Khanna, Bijaya Ketan Panigrahi, Anupam Joshi , AI based approach to identify compromised meters in data integrity attacks on smart grid, Volume 12, Issue 5, 02 October 2017, DOI: 10.1049/iet-gtd.2017.0455en_US
dc.identifier.uri10.1049/iet-gtd.2017.0455
dc.identifier.urihttp://hdl.handle.net/11603/11570
dc.language.isoen_USen_US
dc.publisherIETen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsThis paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.
dc.subjectartificial intelligenceen_US
dc.subjectsmart power gridsen_US
dc.subjectsecurity of dataen_US
dc.subjectlearning (artificial intelligence)en_US
dc.subjectpower system securityen_US
dc.subjectpower engineering computingen_US
dc.subjectcyber-threatsen_US
dc.subjectcompromised metre identificationen_US
dc.subjectKnowledge engineering techniquesen_US
dc.subjectData securityen_US
dc.subjectPower system controlen_US
dc.subjectPower engineering computingen_US
dc.subjectfalse data injection attacksen_US
dc.subjectdata integrity attacksen_US
dc.subjectsmart griden_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleAI based approach to identify compromised meters in data integrity attacks on smart griden_US
dc.typeTexten_US

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