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dc.contributor.authorIsmail, Muhammad
dc.contributor.authorShaaban, Mostafa
dc.contributor.authorNaidu, Mahesh
dc.contributor.authorSerpedin, Erchin
dc.date.accessioned2022-02-08T12:55:42Z
dc.date.available2022-02-08T12:55:42Z
dc.date.issued2020-02
dc.identifier.citationM. Ismail, M. F. Shaaban, M. Naidu and E. Serpedin, "Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation," in IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3428-3437, July 2020, doi: 10.1109/TSG.2020.2973681.en_US
dc.identifier.issn1949-3061
dc.identifier.urihttp://hdl.handle.net/11073/21634
dc.description.abstractUnlike the existing research that focuses on detecting electricity theft cyber-attacks in the consumption domain, this paper investigates electricity thefts at the distributed generation (DG) domain. In this attack, malicious customers hack into the smart meters monitoring their renewable-based DG units and manipulate their readings to claim higher supplied energy to the grid and hence falsely overcharge the utility company. Deep machine learning is investigated to detect such a malicious behavior. We aim to answer three main questions in this paper: a) What are the cyber-attack functions that can be applied by malicious customers to the generation data in order to falsely overcharge the utility company? b) What sources of data can be used in order to detect these cyber-attacks by the utility company? c) Which deep machine learning-model should be used in order to detect these cyber-attacks? Our investigation revealed that integrating various data from the DG smart meters, meteorological reports, and SCADA metering points in the training of a deep convolutional-recurrent neural network offers the highest detection rate (99:3%) and lowest false alarm (0:22%).en_US
dc.description.sponsorshipQatar National Research Funden_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.urihttps://doi.org/10.1109/TSG.2020.2973681en_US
dc.subjectDistributed generationen_US
dc.subjectElectricity theften_US
dc.subjectDeep machine learningen_US
dc.subjectHyper-parameter optimizationen_US
dc.titleDeep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generationen_US
dc.typeArticleen_US
dc.typePeer-Revieweden_US
dc.typePostprinten_US
dc.identifier.doi10.1109/TSG.2020.2973681


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