Evaluating Detectors on Optimal Attack Vectors That Enable Electricity Theft and DER Fraud

Krishna, Varun Badrinath; Gunter, Carl A.; Sanders, William H.

doi:10.1109/jstsp.2018.2833749

Cited by 62 publications

(35 citation statements)

References 21 publications

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“…The deployment of distributed energy resources (DERs) is increasing day by day. The study in [46] presented meter fraud detection in DERs and provided a set of electricity fraud attacks. An assumption is made in this paper that the electricity thief is aware of the ETD method.…”

Section: Related Workmentioning

confidence: 99%

Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

et al. 2021

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In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset's dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models' performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.

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Section: Related Workmentioning

confidence: 99%

Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

et al. 2021

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“…In parallel, the integration of DRES involves diverse types of data communication and systemon-chip technologies that are commonly manufactured with minimal security [23], [24]. Hence, enlarging the spectrum of cyber attacks that could be initiated such as to support potential energy theft acts [25].…”

Section: ) Distributed Renewable Energy Sources (Dres)mentioning

confidence: 99%

Energy Theft in Smart Grids: A Survey on Data-Driven Attack Strategies and Detection Methods

et al. 2021

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The convergence of legacy power system components with advanced networking and communication facilities have led towards the development of smart grids. Smart grids are envisioned to be the next generation innovative power systems, guaranteeing resilience, reliability and sustainability and to facilitate energy production, distribution and management. Nonetheless, the development of such systems entails challenges covering a broad spectrum ranging from operational management up to datadriven power accounting and network security. Given the highly distributed properties of the modern grid, energy theft can now be observed at various transmission and distribution levels. Apart from the financial gain for a malicious actor, energy theft can also affect critical grid processes with a direct impact on its overall resilience and safety. This survey reviews recent energy theft strategies as well as detection methods from a data-driven perspective. By considering various operational and functional layers within modern smart grids we critically assess how energy theft can be formulated. Moreover, we provide an overview of the grid demand, supply and control chain with a focus on energy theft and associated security flaws that currently exist in the smart grid ecosystem. Different attack detection models for theft detection in the smart grid are categorized. Lastly, we discuss various open issues in the scope of data-driven energy theft detection methods and provide future directions to carry out research in this field.

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“…The work in [13] is extended in [14] to evaluate signal processing-based detection approaches for meter frauds in alternate resources like solar and wind grid data.…”

Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Preethi¹,

K²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Cyber Physical systems such as smart grids have the potential to address the future energy crisis. Because of the bidirectional flow of information across various domains in a smart grid, anomaly detection is one of the prime security related challenges. Machine learning models have emerged as one of the prospective artificial intelligence technologies to model supervised and unsupervised data for analysis and prediction. This paper reviews the various anomaly detection schemes in a Smart Grid Infrastructure based on machine learning techniques.

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Evaluating Detectors on Optimal Attack Vectors That Enable Electricity Theft and DER Fraud

Cited by 62 publications

References 21 publications

Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

Energy Theft in Smart Grids: A Survey on Data-Driven Attack Strategies and Detection Methods

An Introductory Review Of Anomaly Detection Methods In Smart Grids

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