| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-04-30 |
| タイトル |
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タイトル |
Electricity Theft Detection for Smart Homes: Harnessing the Power of Machine Learning with Real and Synthetic Attacks |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Electricity theft detection |
| キーワード |
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主題Scheme |
Other |
|
主題 |
machine learning |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
synthetic attack data |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
smart home |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
real attack data |
| キーワード |
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主題Scheme |
Other |
|
主題 |
unsupervised learning |
| キーワード |
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主題Scheme |
Other |
|
主題 |
supervised learning |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Abraham, Olufemi Abiodun
Ochiai, Hideya
Hossain, Md. Delwar
妙中, 雄三
門林, 雄基
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Electricity theft is a pervasive issue with economic implications that necessitate innovative approaches for its detection, given the critical challenge of limited labeled data. However, connecting smart home devices introduces numerous vectors for electricity theft. Therefore, this study introduces an innovative approach to detecting electricity theft in smart homes, leveraging knowledge-based, fine-grained, time-series appliance benign and anomalous consumption patterns. We simulated five attack classes and extended our model’s detection capabilities to unknown anomalies across residential settings by segmenting the anonymized data into three different home categories. We validated our experiment using simulated and real building attack data. Extreme Gradient Boost (XGB), Random Forest, and Multilayer Perceptron (MLP) outperform the legacy unsupervised model (LUM), which included MLP-Autoencoder (AE), 1D-CONV-AE, and Isolation Forest (RF). XGB had the highest average AUC scores of 98.69% and 98.74% for simulated and real attack detection, respectively, followed by RF at 96.76% and 97.07%, respectively, across all homes, indicating the robustness of our model in detecting benign and anomalous appliance consumption patterns. This study contributes to the academic discourse in the field and offers practical solutions to energy providers and stakeholders in the smart home industry. |
| 書誌情報 |
en : IEEE Access
巻 12,
p. 26023-26045,
発行日 2024-02-14
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| 出版者 |
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出版者 |
IEEE |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2169-3536 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1109/ACCESS.2024.3366493 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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|
識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/abstract/document/10436697 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
c 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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|
助成機関名 |
Industrial Cyber Security Centre of Excellence (ICSCoE) |
