Anomaly detection of smart grid stealing network attacks based on deep autoencoder
Existing anomaly detectors in AMIs suffer from shallow architectures, which impede their ability to capture temporal correlations and complex patterns in electricity consumption data, thus impact detection performance adversely. A deep (stacked) autoencoder structure based on Long Short-Term Memory...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
National Computer System Engineering Research Institute of China
2024-02-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000163482 |
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| Summary: | Existing anomaly detectors in AMIs suffer from shallow architectures, which impede their ability to capture temporal correlations and complex patterns in electricity consumption data, thus impact detection performance adversely. A deep (stacked) autoencoder structure based on Long Short-Term Memory (LSTM) with a sequence-to-sequence (seq2seq) configuration is proposed. The depth of the autoencoder architecture is beneficial for capturing complex data patterns, and the seq2seq LSTM model effectively utilizes the temporal sequential characteristics of the data. The performance of simple autoencoders, variational autoencoders, and Attention Enhanced Autoencoders (AEA) was studied, revealing that using the seq2seq structure in these three types of autoencoders results in superior detection performance compared to fully connected architectures. Simulation results demonstrate that the detector with an attention mechanism (AEA) achieves a 4%~21% higher detection rate and a 4%~13% lower false alarm rate compared to the best-performing existing detectors. |
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| ISSN: | 0258-7998 |