Detection and identification of energy theft in advanced metering infrastructures

Souza, Matheus Almeida; Pereira, J.L.R.; Alves, Guilherme de Oliveira; Oliveira, Bráulio César de; Melo, Igor D.; Garcia, P.A.N.

doi:10.1016/j.epsr.2020.106258

Cited by 52 publications

(26 citation statements)

References 12 publications

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“…Zhang [16] proposed a system that detects intrusions by learning network packet data with an Extreme Learning Machine (ELM). Furthermore, Souza [17] proposed a method by which to detect energy theft by modeling residential and commercial consumers using Self-Organizing Maps (SOM) and Multiplayer Perceptron (MP)-ANN and learning the consumers' energy consumption rates per hour. Bendiab [18] proposed a framework that visualizes AMI network traffic in images using the Binivis technique and detects malicious codes through classification algorithms.…”

Section: Security On Advanced Metering Infrastructurementioning

confidence: 99%

Cyber Threat Intelligence Framework for Incident Response in an Energy Cloud Platform

Gong

Lee

2021

Electronics

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Advanced information technologies have transformed into high-level services for more efficient use of energy resources through the fusion with the energy infrastructure. As a part of these technologies, the energy cloud is a technology that maximizes the efficiency of energy resources through the organic connection between the entities that produce and consume the energy. However, the disruption or destruction of energy cloud systems through cyberattacks can lead to incidents such as massive blackouts, which can lead to national disasters. Furthermore, since the technique and severity of modern cyberattacks continue to improve, the energy cloud environment must be designed to resist cyberattacks. However, since the energy cloud environment has different characteristics from general infrastructures such as the smart grid and the Advanced Metering Infrastructure (AMI), it requires security technology specialized to its environment. This paper proposes a cyber threat intelligence framework to improve the energy cloud environment’s security. Cyber Threat Intelligence (CTI) is a technology to actively respond to advanced cyber threats by collecting and analyzing various threat indicators and generating contextual knowledge about the cyber threats. The framework proposed in this paper analyzes threat indicators that can be collected in the advanced metering infrastructure and proposes a cyber threat intelligence generation technique targeting the energy cloud. This paper also proposes a method that can quickly apply a security model to a large-scale energy cloud infrastructure through a mechanism for sharing and spreading cyber threat intelligence between the AMI layer and the cloud layer. Our framework provides a way to effectively apply the proposed technologies through the CTI architecture, including the local AMI layer, the station layer, and the cloud layer. Furthermore, we show that the proposed framework can effectively respond to cyber threats by showing a 0.822 macro-F1 score and a 0.843 micro-F1 score for cyberattack detection in an environment that simulates a model of an attacker and an energy cloud environment.

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Section: Security On Advanced Metering Infrastructurementioning

confidence: 99%

Cyber Threat Intelligence Framework for Incident Response in an Energy Cloud Platform

Gong

Lee

2021

Electronics

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“…Although the experimental accuracy was 89%, in practice, energy thieves are often far less accurate than normal users. e work in [23] developed a new method to detect and identify energy theft in distribution systems using a multilayer perceptron artificial neural network (MP-ANN) algorithm. ey successfully classified malicious users and normal users with an average accuracy of 93.4%.…”

Section: Related Workmentioning

confidence: 99%

Energy Theft Detection in an Edge Data Center Using Deep Learning

Cheng

Zhang

Li³

et al. 2021

Mathematical Problems in Engineering

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With the development of smart grid information physical systems, some of the data processing functions gradually approach the edge layer of end-users. To better realize the energy theft detection function at the edge, we proposed an energy theft detection method based on the power consumption information acquisition system of power enterprises. The method involves the following steps. In the centralized data center, K-means is used to decompose a large amount of data into small data and then input and train neural network parameters to realize feature extraction. We design a neural network named DWMCNN, which can extract features from the day, week, and month and can extract more accurate features. In the edge data center, the random forest (RF) algorithm is used to classify the extracted features. The experimental results show that the clustering method accords with the idea of edge computing-distributed processing and improves the operation speed and that the feature extractor has good convergence performance. In addition, compared with the methods based on various classifiers, this method has higher accuracy and lower computational complexity, which is suitable for the deployment of edge data centers.

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“…However, it is unavoidable to use an adequate preprocessing method. These researchers in [14] developed a regression model using hybrid structures such as a CNN-LSTM model on energy consumption datasets. This article solved the classification issue by combining the CNN-LSTM hybrid model with a preprocessing method on the power consumption pattern dataset.…”

Section: A Problem Statementmentioning

confidence: 99%

A Novel Method CNN-LSTM Ensembler Based on Black Widow and Blue Monkey Optimizer for Electricity Theft Detection

Almazroi

Ayub²

2021

IEEE Access

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Enhanced metering infrastructure is a key component of the electrical system, offering many advantages, including load management and demand response. However, several additional energy theft channels are introduced by the automation of the metering system. With data analysis techniques, adapting the smart grid significantly reduces energy theft loss. In this article, we proposed deep learning methods for the identification of power theft. A three-stage technique has been devised, which includes selection, extraction, and classification of features. In the selection phase, the average hybrid feature importance determines the most important features and high priority. The feature extraction technique employs the ZFNET method to remove the unwanted features. For the detection of electric fraud, Convolutional Neural Network based Long Short Term Memory (CNN-LSTM) is utilized. Meta-heuristic techniques, including Black Widow Optimization (BWO) and Blue Monkey Optimization (BMO), are used to calculate optimized values for the hyperparameters of CNN-LSTM. The tuning of hyperparameters of the classifier helps in better training on data. After extensive simulation, our proposed methods CNN-LSTM-BMO and CNN-LSTM-BWO achieved an accuracy of 91% and 93%. Our proposed methods outperform all the existing compared schemes. The performance of our models has attained high accuracy and low error rate. Furthermore, the statistical analysis also shows the superiority of the proposed methods.

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Detection and identification of energy theft in advanced metering infrastructures

Cited by 52 publications

References 12 publications

Cyber Threat Intelligence Framework for Incident Response in an Energy Cloud Platform

Cyber Threat Intelligence Framework for Incident Response in an Energy Cloud Platform

Energy Theft Detection in an Edge Data Center Using Deep Learning

A Novel Method CNN-LSTM Ensembler Based on Black Widow and Blue Monkey Optimizer for Electricity Theft Detection

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