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Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing
In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused dat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609745/ https://www.ncbi.nlm.nih.gov/pubmed/36298168 http://dx.doi.org/10.3390/s22207818 |
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author | Munawar, Shoaib Javaid, Nadeem Khan, Zeshan Aslam Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Milyani, Ahmad H. Ahmed Azhari, Abdullah |
author_facet | Munawar, Shoaib Javaid, Nadeem Khan, Zeshan Aslam Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Milyani, Ahmad H. Ahmed Azhari, Abdullah |
author_sort | Munawar, Shoaib |
collection | PubMed |
description | In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors. |
format | Online Article Text |
id | pubmed-9609745 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96097452022-10-28 Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing Munawar, Shoaib Javaid, Nadeem Khan, Zeshan Aslam Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Milyani, Ahmad H. Ahmed Azhari, Abdullah Sensors (Basel) Article In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors. MDPI 2022-10-14 /pmc/articles/PMC9609745/ /pubmed/36298168 http://dx.doi.org/10.3390/s22207818 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Munawar, Shoaib Javaid, Nadeem Khan, Zeshan Aslam Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Milyani, Ahmad H. Ahmed Azhari, Abdullah Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing |
title | Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing |
title_full | Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing |
title_fullStr | Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing |
title_full_unstemmed | Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing |
title_short | Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing |
title_sort | electricity theft detection in smart grids using a hybrid bigru–bilstm model with feature engineering-based preprocessing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609745/ https://www.ncbi.nlm.nih.gov/pubmed/36298168 http://dx.doi.org/10.3390/s22207818 |
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