Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation
Unlike 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 u...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/21634 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513441223409664 |
|---|---|
| author | Ismail, Muhammad |
| author2 | Shaaban, Mostafa Naidu, Mahesh Serpedin, Erchin |
| author2_role | author author author |
| author_facet | Ismail, Muhammad Shaaban, Mostafa Naidu, Mahesh Serpedin, Erchin |
| author_role | author |
| dc.creator.none.fl_str_mv | Ismail, Muhammad Shaaban, Mostafa Naidu, Mahesh Serpedin, Erchin |
| dc.date.none.fl_str_mv | 2020-02 2022-02-08T12:55:42Z 2022-02-08T12:55:42Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | M. 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. 1949-3061 http://hdl.handle.net/11073/21634 10.1109/TSG.2020.2973681 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/TSG.2020.2973681 |
| dc.subject.none.fl_str_mv | Distributed generation Electricity theft Deep machine learning Hyper-parameter optimization |
| dc.title.none.fl_str_mv | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| dc.type.none.fl_str_mv | Peer-Reviewed Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Unlike 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%). |
| format | article |
| id | aus_22dabaadc004b70fbe28c016bc00d137 |
| identifier_str_mv | M. 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. 1949-3061 10.1109/TSG.2020.2973681 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21634 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed GenerationIsmail, MuhammadShaaban, MostafaNaidu, MaheshSerpedin, ErchinDistributed generationElectricity theftDeep machine learningHyper-parameter optimizationUnlike 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%).Qatar National Research FundIEEE2022-02-08T12:55:42Z2022-02-08T12:55:42Z2020-02Peer-ReviewedPostprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfM. 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.2020.2973681.1949-3061http://hdl.handle.net/11073/2163410.1109/TSG.2020.2973681en_UShttps://doi.org/10.1109/TSG.2020.2973681oai:repository.aus.edu:11073/216342024-08-22T12:08:22Z |
| spellingShingle | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation Ismail, Muhammad Distributed generation Electricity theft Deep machine learning Hyper-parameter optimization |
| status_str | publishedVersion |
| title | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| title_full | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| title_fullStr | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| title_full_unstemmed | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| title_short | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| title_sort | Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation |
| topic | Distributed generation Electricity theft Deep machine learning Hyper-parameter optimization |
| url | http://hdl.handle.net/11073/21634 |