基于无监督BLSTM的训练数据污染电盗窃检测

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2023-09-15 DOI:10.1145/3604432
Qiushi Liang, Shengjie Zhao, Jiangfan Zhang, Hao Deng
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引用次数: 0

摘要

电力盗窃会造成经济损失,甚至增加停电的风险。近年来,许多方法都对智能电表数据进行了窃电检测。然而,如何在没有任何标签的情况下对数据集进行检测仍然是一个挑战。本文在假设训练集受到攻击污染的情况下,提出了一种新的无监督两阶段方法。具体而言,该方法包括两个阶段:1)采用高斯混合模型(GMM)对不同用电习惯的消费模式进行聚类,并在后验阶段提高模型的准确性;2)采用基于注意的双向长短期记忆(BLSTM)编码器-解码器方案,利用编码和解码过程提高对非恶意使用模式变化的鲁棒性。通过对消费模式的相似性和重构误差进行量化,定义了异常评分以提高检测性能。在真实数据集上的实验表明,该方法优于目前最先进的无监督检测器。
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Unsupervised BLSTM Based Electricity Theft Detection with Training Data Contaminated
Electricity theft can cause economic damage and even increase the risk of outage. Recently, many methods have implemented electricity theft detection on smart meter data. However, how to conduct detection on the dataset without any label still remains challenging. In this paper, we propose a novel unsupervised two-stage approach under the assumption that the training set is contaminated by attacks. Specifically, the method consists of two stages: 1) A Gaussian mixture model (GMM) is employed to cluster consumption patterns with respect to different habits of electricity usage, and with the goal of improving the accuracy of the model in the posterior stage; 2) An attention-based bidirectional Long Short-Term Memory (BLSTM) encoder-decoder scheme is employed to improve the robustness against the non-malicious changes in usage patterns leveraging the process of encoding and decoding. Quantifying the similarity of consumption patterns and reconstruction errors, the anomaly score is defined to improve detection performance. Experiments on a real dataset show that the proposed method outperforms the state-of-the-art unsupervised detectors.
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来源期刊
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
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