Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids

Takiddin, Abdulrahman; Ismail, Muhammad; Zafar, Usman; Serpedin, Erchin

doi:10.1109/tsg.2020.3047864

Cited by 64 publications

(22 citation statements)

References 22 publications

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“…However, the fluctuations and variations in the normal and theft load profiles are not properly detected, which yield poor detection results. Similarly, the authors in [20] analyze some traditional techniques that are applied to detect data poisoning attacks. However, these techniques add up an additional stage of data filtering, which first removes any available false label and then performs the detection step.…”

Section: Related Workmentioning

confidence: 99%

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

et al. 2022

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In this paper, we present a hybrid deep learning model that is based on a two-dimensional convolutional neural network (2D-CNN) and a bidirectional long short-term memory network (Bi-LSTM)to detect non-technical losses (NTLs) in smart meters. NTLs occur due to the fraudulent use of electricity. The global integration of smart meters has proven to be beneficial for the storage of historical electricity consumption (EC) data. The proposed methodology learns the deep insights from the historical EC data and informs power utilities about the presence of NTLs. However, the effective detection of NTLs faces the problem of class imbalance that occurs due to the rare availability of fraudulent electricity consumers. To solve this issue, an evolutionary bidirectional Wasserstein generative adversarial network (Bi-WGAN) is employed. Bi-WGAN synthesizes the most plausible fraudulent EC samples by integrating an auxiliary encoder module. Besides, the inevitable curse of high dimensional data reduces the generalization ability of classifiers. The proposed hybrid model efficiently handles the highly dynamic data by utilizing its potent feature extracting capabilities. The one-dimensional daily EC data is passed to Bi-LSTM model for capturing the non-malicious changes from consumers' profiles. Meanwhile, 2D-CNN takes 2D weekly EC data as input to extract the potential features by applying different convolutions and pooling operations. Extensive experiments are conducted on a realistic smart meters dataset to prove the effectiveness of the proposed model. The results show that the proposed model outperforms the state-of-the-art models by achieving area under the curve receiver operating characteristics of 0.97 and precision-recall area under the curve of 0.98, which make it suitable for real-world scenarios.

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Section: Related Workmentioning

confidence: 99%

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

et al. 2022

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“…MAP is typically used to estimate the quality of information retrieval. To calculate the MAP, we need to define P@S, as in (25).…”

Section: B Evaluation Metrics and Comparisonsmentioning

confidence: 99%

“…Second, deep learning-based methods demand massive, trustworthy samples for model training. However, the unbalanced dataset and contaminated samples [25] significantly affect their accuracies. Moreover, they may mistake some normal activities such as meter installation and climate change as theft behaviors.…”

Section: Introductionmentioning

confidence: 99%

A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss

et al. 2022

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Owing to the contagiousness of theft behaviors among customers, collaborative energy theft, such as village fraud, has become particularly common. In this study, a bunch of electricity thieves that steal energy at a constant ratio were considered. Conventional correlation-sorting-based methods may have some trouble handling these electricity thieves when they exist in the same area. To overcome such limitation, we firstly establish the mathematical model of non-technical loss (NTL) and the load data of fixed ratio electricity thieves (FRETs). Subsequently, an interesting correlation trend, which can be exploited to locate FRETs, was observed and analyzed. Based on this trend, we propose a correlation analysis-based detection method. It adopts a standardized covariance to measure the correlation between the NTL and user data. The detection of FRETs is realized by solving a combinatorial optimization problem. A corresponding framework in practice was also designed. Finally, numerical experiments based on a realistic

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“…Takiddin et al in [8] provided answers to three major questions pertaining to the performance of electricity theft detectors in the presence of data poisoning attacks. By proposing a sequential ensemble detector based on a deep autoencoder with attention (AEA), gated recurrent units (GRUs), and feed forward neural networks.…”

Section: Related Workmentioning

confidence: 99%

Malicious Node Detection in Smart Grid Networks

Yahmadi¹,

Ahmed²

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Many countries around the world are implementing smart grids and smart meters. Malicious users that have moderate level of computer knowledge can manipulate smart meters and launch cyber-attacks. This poses cyber threats to network operators and government security. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we propose a model based on software that detects malicious nodes in a smart grid network. The model collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) model is implemented to classify nodes into good or malicious nodes by (high dimensional) giving the statues of 1 for good nodes and status of -1 for malicious (abnormal) nodes. The detection model also displays the network graphically as well as the data table. Moreover, this model displays the detection error in each cycle. It has a very low false alarm rate (2%) and a high detection rate as high as (98%). Future developments can trace the attack origin to eliminate or block the attack source minimizing losses before human control arrives.

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Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids

Cited by 64 publications

References 22 publications

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss

Malicious Node Detection in Smart Grid Networks

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