中圖分類號(hào)：TM28 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234395
中文引用格式： 黃燕,，李金燦，楊霞琴,，等. 基于深度自編碼器的智能電網(wǎng)竊電網(wǎng)絡(luò)攻擊異常檢測(cè)[J]. 電子技術(shù)應(yīng)用,，2024,，50(2)：76-82.
英文引用格式： Huang Yan,，Li Jincan，Yang Xiaqin,，et al. Anomaly detection of smart grid stealing network attacks based on deep autoencoder[J]. Application of Electronic Technique,，2024，50(2)：76-82.

Anomaly detection of smart grid stealing network attacks based on deep autoencoder

Abstract： 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.

Key words : autoencoder；deep machine learning,；power stealing,；hyperparameter optimization；sequence-to-sequence