Theft Detection Research Articles

Boruta-XGBoost Electricity Theft Detection Based on Features of Electric Energy Parameters

Electricity theft detection is critical for the safe and effective development of the electric power system. The existing methods that are used to detect electricity theft rely on historical load data and are considered to have poor timeliness. Their detection results have limited reference to power supply enterprises’ investigation on electricity theft. Therefore, this paper proposes the Boruta-XGBoost power theft detection model based on multiple features of electric energy parameters. The model converts electricity theft detection problem into a multiclass classification problem. First, the features of various electric energy parameters that are collected by the electric energy metering device are used to construct the original dataset. Then, the Boruta algorithm is used to select features and reconstruct the dataset based on the selection results. Finally, the reconstructed dataset is used to train an XGBoost model that can detect the type of electricity theft based on the features of real-time electric energy parameters. In order to verify the effectiveness of the model, a comparative experiment is conducted in this paper. The reconstructed dataset is used to train the following algorithms: LightGBM, CART, SVM, and logistic regression models. The test results show that the Boruta-XGBoost model in this paper has the best effect on electricity theft detection.

Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation

Most of the existing research focuses on electricity theft cyberattacks in the consumption domain. On the contrary, a high penetration level of distributed generators (DGs) may result in increased electricity theft cyberattacks in the distributed generation domain, which is the focus of this article. In these attacks, malicious customers can hack into the smart meters monitoring their DG units, which are usually photovoltaic, and manipulate their readings to report higher injected energy to the grid and claim more profit under feed-in tariff programs. This article proposes a data-driven approach based on machine learning to detect such thefts. We adopt an anomaly detection approach where a theft detection unit (TDU) based on a regression tree model is designed to detect suspicious data. Historical records of solar irradiance, temperature, and smart meter readings are utilized in the training stage of the detector. The probability density function of the error between the actual readings from DG meters and the predicted generation by the regression model is utilized as a metric to detect suspicious data. Several theft scenarios are used to assess the performance of the TDU. Furthermore, a comparison study with other detectors is presented to demonstrate the superiority of the proposed TDU.

GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection.

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.

Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment

Smart meters are key elements of a smart grid. These data from Smart Meters can help us analyze energy consumption behaviour. The machine learning and deep learning approaches can be used for mining the hidden theft detection information in the smart meter data. However, it needs effective data extraction. This research presents a theft detection dataset (TDD2022) and a machine learning-based solution for automated theft identification in a smart grid environment. An effective theft generator is modelled and used for obtaining a multi-class theft detection dataset from publicly available consumer energy consumption data, owned by the “Open Energy Data Initiative” (OEDI) platform. This is an important and interesting phase to explore in the smart grid field. The proposed dataset can be used for benchmarking and comparative studies. We evaluated the proposed dataset using five different machine learning techniques: k-nearest neighbours (KNN), decision trees (DT), random forest (RF), bagging ensemble (BE), and artificial neural networks (ANN) with different evaluation alternatives (mechanisms). Overall, our best empirical results have been recorded to the theft detection-based RF model scoring an improvement in the performance metrics by 10% or more over the other developed models.

A Control-Chart-Based Detector for Small-Amount Electricity Theft (SET) Attack in Smart Grids

For achieving the goal of two-way communication and power flows, smart grids are integrated with much state-of-the-art hardware and software. However, these newly added components also introduce a lot of vulnerabilities into the power systems, which results in that malicious users can launch various cyber-physical attacks to steal electricity. Existing electricity theft detection techniques suffer from an implicit assumption that malicious users tamper with smart meter readings to values much less than their actual electricity consumptions. These are called Large-amount Electricity Theft (LET) attacks. Nevertheless, in the real world, some malicious users may be cautious enough to deliberately launch Small-amount Electricity Theft (SET) attacks where smart meter readings are manipulated to numbers slightly lower than actual values, mainly to escape detection. To address this limitation, we propose a detector able to deal with both LET and SET attacks effectively. This detector applies a cumulative sum (CUSUM) control chart and a Shewhart control chart together to analyze users’ reported readings and measurements of a central observer meter. It consists of an electricity theft detection phase which aims to detect the existence of LET/SET attacks timely and a malicious user identification phase which aims to identify malicious users exactly. Extensive experiments are conducted to evaluate the proposed detector, and the results show that it has good performance in terms of several metrics.

Two-Step Electricity Theft Detection Strategy Considering Economic Return Based on Convolutional Autoencoder and Improved Regression Algorithm

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms.

FedDetect: A Novel Privacy-Preserving Federated Learning Framework for Energy Theft Detection in Smart Grid

In smart grids, a major challenge is how to effectively utilize consumers’ energy consumption data while preserving security and privacy. In this article, we tackle this challenging issue and focus on energy theft detection, which is very important for smart grids. Specifically, we note that most existing energy theft detection schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. To address this issue, we propose a novel privacy-preserving federated learning framework for energy theft detection, namely, FedDetect. In our framework, we consider a federated learning system that consists of a data center (DC), a control center (CC), and multiple detection stations. In this system, each detection station (DTS) can only observe data from local consumers, which can use a local differential privacy (LDP) scheme to process their data to preserve privacy. To facilitate the training of the model, we design a secure protocol so that detection stations can send encrypted training parameters to the CC and the DC, which then use homomorphic encryption to calculate the aggregated parameters and return updated model parameters to detection stations. In our study, we prove the security of the proposed protocol with solid security analysis. To detect energy theft, we design a deep learning model based on the state-of-the-art temporal convolutional network (TCN). Finally, we conduct extensive data-driven experiments using a real-energy consumption data set. The experimental results demonstrate that the proposed federated learning framework can achieve high accuracy of detection with a smaller computation overhead.

Electricity theft detection using big data and genetic algorithm in electric power systems

Non-technical losses (NTLs) are one of the major causes of revenue losses for electric utilities. In the literature, various machine learning (ML)/deep learning (DL) approaches are employed to detect NTLs. The existing studies are mostly concerned with tuning the hyperparameters of ML/DL methods for efficient detection of NTL, i.e., electricity theft detection. Some of them focus on the selection of prominent features from data to improve the performance of electricity theft detection. However, the curse of dimensionality affects the generalization ability of ML/DL classifiers and leads to computational, storage, and overfitting problems. Therefore, to deal with the above-mentioned issues, this study proposes a system based on metaheuristic techniques (artificial bee colony and genetic algorithm) and denoising autoencoder for electricity theft detection using big data in electric power systems. The former (metaheuristics) are used to select prominent features, while the latter is utilized to extract high variance features from electricity consumption data. Firstly, 11 new features are synthesized using statistical and electrical parameters from the user’s consumption history. Then, the synthesized features are used as input to metaheuristic techniques to find a subset of optimal features. Finally, the optimal features are fed as input to the denoising autoencoder to extract features with high variance. The ability of both metaheuristic and autoencoder techniques to select and extract features is measured using a support vector machine. The proposed system reduces the overfitting, storage, and computational overhead of ML classifiers. Moreover, we perform several experiments to verify the effectiveness of our proposed system and results reveal that the proposed system has better performance than its counterparts.

Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

Received Signal Strength Indicator (Rssi) Based Car Theft Detection System

Received Signal Strength Indicator (Rssi) Based Car Theft Detection System

Energy Theft Detection in Smart Grids: Taxonomy, Comparative Analysis, Challenges, and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly. Especially with the introduction of emerging technologies, this issue became more complicated. Though many new energy theft detection (ETD) techniques have been proposed by utilising different data mining (DM) techniques, state & network (S&N) based techniques, and game theory (GT) techniques. Here, a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations. Three levels of taxonomy are presented to classify state-of-the-art ETD techniques. Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature. The challenges of different ETD techniques and their mitigation are suggested for future work. It is observed that the literature on ETD lacks knowledge management techniques that can be more effective, not only for ETD but also for theft tracking. This can help in the prevention of energy theft, in the future, as well as for ETD.

Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks

In this work, we aim at building a bridge from coarse behavioral data to an effective, quick-response, and robust behavioral model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multidimensional low-quality data, e.g., offline check-ins and online user-generated content (UGC). As an insightful result, we validate that there is a complementary effect among different dimensions of records for modeling users’ behavioral patterns. To deeply exploit such a complementary effect, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint</i> (instead of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fused</i> ) model to capture both online and offline features of a user’s composite behavior. We evaluate the proposed joint model by comparing it with typical models and their fused model on two real-world datasets: Foursquare and Yelp. The experimental results show that our model outperforms the existing ones, with the area under the receiver operating characteristic curve (AUC) values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> (true positive rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the corresponding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">disturbance rate</i> (false-positive rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how real-time online identity authentication can be improved via modeling users’ composite behavioral patterns.

Online electricity theft detection framework for large-scale smart grid data

Smart grid gives more control and information to the utility companies. However, it can be leveraged for data manipulation, which can lead to new techniques in electricity theft. This paper presents an electricity theft detection framework, designed for handling real-time large-scale smart grid data to address these new emerging threats. It uses a hybrid approach, combining the information inferred by analyzing the reported data from distribution transformer meters with machine learning algorithms to discover fraudulent activity. We added an additional form of attack to the six previously known patterns and generated malicious variants of consumption data to solve the problem of imbalanced dataset classes, resulting in more accurate classifiers. The framework also allows for a trade-off between the detection rate and triggered false alarms by using a sliding window in the decision-making process. In the end, the proposed framework is evaluated using well-known clustering and classification methods in a practical scenario, resulting in outcomes superior or equal to the previously achieved scores while having the advantages of online and distributed processing.

A Stacked Machine and Deep Learning-Based Approach for Analysing Electricity Theft in Smart Grids

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for theft detection can struggle to handle large electricity consumption datasets because of missing values, data variance and nonlinear data relationship problems, and there is a lack of integrated infrastructure for coordinating electricity load data analysis procedures. To help address these problems, a simple yet effective ETD model is developed. Three modules are combined into the proposed model. The first module deploys a combination of data imputation, outlier handling, normalization and class balancing algorithms, to enhance the time series characteristics and generate better quality data for improved training and learning by the classifiers. Three different machine learning (ML) methods, which are uncorrelated and skillful on the problem in different ways, are employed as the base learning model. Finally, a recently developed deep learning approach, namely a temporal convolutional network (TCN), is used to ensemble the outputs of the ML algorithms for improved classification accuracy. Experimental results confirm that the proposed framework yields a highly-accurate, robust classification performance, in comparison to other well-established machine and deep learning models and thus can be a practical tool for electricity theft detection in industrial applications.

BiGRU-CNN Neural Network Applied to Electric Energy Theft Detection

This paper presents an assessment of the potential behind the BiGRU-CNN artificial neural network to be used as an electric power theft detection tool. The network is based on different architecture layers of the bidirectional gated recurrent unit and convolutional neural network. The use of such a tool with this classification model can help energy sector companies to make decisions regarding theft detection. The BiGRU-CNN artificial neural network singles out consumer units suspected of fraud for later manual inspections. The proposed artificial neural network was programmed in python, using the keras package. The best detection model was that of the BiGRU-CNN artificial neural network when compared to multilayer perceptron, recurrent neural network, gated recurrent unit, and long short-term memory networks. Several tests were carried out using data of an actual electricity supplier, showing the effectiveness of the proposed approach. The metric values assigned to their classifications were 0.929 for accuracy, 0.885 for precision, 0.801 for recall, 0.841 for F1-Score, and 0.966 for area under the receiver operating characteristic curve.

Designing a blockchain-enabled privacy-preserving energy theft detection system for smart grid neighborhood area network

In smart grid neighbourhood area network (NAN), the grid utility faces a huge economic loss due to smart meter tampering and energy theft. The faulty meters may increase the cost to consumers. The existing solutions lack in detecting the energy theft accurately and preserving the privacy of the consumers. In this paper, a blockchain-enabled energy theft detection system with privacy-preservation of energy consumption data is proposed for smart grid NAN. It deploys smart contracts viz; energy supply, energy consumption and energy theft detection on the hyperledger besu. Here, the supplied energy to the consumers is monitored using the energy supply contract, whereas the energy consumption is reported to the grid utility through energy consumption contract that preserves privacy of the consumers’ energy consumption data. The energy theft detection contract detects the energy theft and faulty meters accurately. The proposed system is validated using a test-bed at NIT Goa by considering the test scenario of energy theft and faulty smart meters. It achieves >98% accuracy in energy theft detection with sufficient throughput (>98.37 tps) and an affordable latency (<0.42 seconds) of energy data transactions, while fulfilling the energy theft detection requirements in smart grid NAN.

A Systematic Review on Supervised Learning Techniques in Electricity Theft Detection

A Systematic Review on Supervised Learning Techniques in Electricity Theft Detection

AN EFFICIENT ELECTRICITY THEFT DETECTION USING XG BOOST

Electricity theft is a significant occurrence that is happening about all nations from one side of the planet to the other. This adversely affects the nation's economy. Theft identification in the power sector is a difficult task for power distribution organizations overall, since this power theft prompts to monetary losses just as loss of electric energy. There are numerous ways by which power is stolen like controlling energy meters or tapping cables at the consumer's end, and so on. Since this robbery is going on in enormous amount, manual examination of such burglary is a hectic task. So, automatic detection of power theft is a vital need of the hour. This paper presents an electricity theft detection model using smart grid meter data dependent on Extreme Gradient Boosting (XGBoost) and OCR. Feature Selection is made used of in the model in order to pick the most relevant features from the dataset to that of our electricity theft detection model. XGBoost is remarkable as it utilizes a more regularized model formalization to have command over-fitting which results in better execution significantly quicker. OCR is employed in order to know what object led to electricity theft detection.

Detection Methods in Smart Meters for Electricity Thefts: A Survey

For accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods.

Adaptive electricity theft detection method based on load shape dictionary of customers

With the application of the advanced measurement infrastructure in power grids, data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves. However, owing to anomaly submergence, which shows that the usage patterns of electricity thieves may not always deviate from those of normal users, the performance of the existing usage-pattern-based method could be affected. In addition, the detection results of some unsupervised learning algorithm models are abnormal degrees rather than “0-1” to ascertain whether electricity theft has occurred. The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users. To address these issues, this study proposes a new electricity theft detection method based on load shape dictionary of users. A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft, and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.