ETD-ConvLSTM: A Deep Learning Approach for Electricity Theft Detection in Smart Grids

In smart grids, various Internet-of-Things-based (IoT-based) components are massively deployed across the power systems. However, most of these IoT-based components have their own vulnerabilities, leveraging which malicious users can launch different cyber/physical attacks to steal electricity. Econ...

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Veröffentlicht in:	IEEE transactions on information forensics and security 2023, Vol.18, p.2553-2568
Hauptverfasser:	Xia, Xiaofang, Lin, Jian, Jia, Qiannan, Wang, Xiaoluan, Ma, Chaofan, Cui, Jiangtao, Liang, Wei
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy convolutional LSTM Correlation deep learning Detectors Economic impact Electric power systems Electricity Electricity theft detection Inspection Internet of Things malicious users Meters Neural networks Smart grid Smart grids Smart meters Theft Time series analysis
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