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Gunduz, M.Z.; Das, R. Smart Grid Security. Encyclopedia. Available online: (accessed on 16 April 2024).
Gunduz MZ, Das R. Smart Grid Security. Encyclopedia. Available at: Accessed April 16, 2024.
Gunduz, Muhammed Zekeriya, Resul Das. "Smart Grid Security" Encyclopedia, (accessed April 16, 2024).
Gunduz, M.Z., & Das, R. (2024, February 27). Smart Grid Security. In Encyclopedia.
Gunduz, Muhammed Zekeriya and Resul Das. "Smart Grid Security." Encyclopedia. Web. 27 February, 2024.
Smart Grid Security

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses.

convolutional neural network cyber security deep learning energy theft

1. Introduction

The development of the Internet has enabled more effective and widespread use of Internet of Things (IoT) applications. IoT enables the connection of different objects to the Internet and the ability to communicate with devices in distant networks [1]. Critical infrastructures such as electricity grids have become IoT-based [2]. Electricity generation, transmission, distribution, and consumption processes have become more manageable in this way. IoT-based electricity systems are called the smart grid. The advanced metering infrastructure (AMI) is the communication network of smart grid applications [3]. The AMI carries sensitive information, making it a potential target for attackers. Due to the inherent vulnerabilities of communication networks, cyber-security emerges as a leading problem in smart grid systems [4].
The daily life of humankind depends on electricity and requires effective management. AMI helps this management by using control commands and real-time transmission of the data to utilities, customers, and third parties. Generally, an AMI system consists of smart meters, gateways, communication networks, and a headend system [5]. The most prominent component of AMI is smart meters. Smart meters increase the frequency of collection of energy consumption data, enabling advanced data analysis that was not possible before [6]. A smart meter records and transmits energy consumption of the customers at specific intervals for billing and management [7]. Unauthorized access to a smart meter may result in data tampering attacks called energy theft [8]. Energy theft is a significant challenge for smart grid applications as malicious actors continue to exploit potential vulnerabilities [9]. Unethical customers represent the highest probability of threats to the AMI and smart meters. In the past, energy theft mainly involved physical disruptions like cut-offs or damage. However, contemporary instances may encompass sophisticated attacker models, including erasing log events, false data injection (FDI) attacks, intercepting communication, and data manipulation [10].
Energy theft is a significant concern for utilities, and it has emerged as a global issue, resulting in technical and economic losses for operators and governments [11]. Deep learning (DL)-based models play a prominent role in the design of effective intrusion detection systems (IDSs). Such IDSs are used to identify abnormal activities such as FDI and data tampering [12]. Energy theft is an important issue that needs to be solved to improve smart grid applications. Also, information and communication technologies (ICTs) and correlated cyber-threats necessitate proactive measures. There are various studies on energy theft detection handling the consumption data to achieve a high detection rate (DR) and accurate results [13][14][15][16][17]. Many methods are used for energy theft detection, such as statistics, data mining, machine learning (ML), and DL techniques [18]. DL-based IDSs play a critical role in identifying energy theft attacks [19].

2. Smart Grid Security

Many vulnerabilities inherited from communication networks exist in AMI.
Understanding the data flow in smart grid applications is significant, and this can be achieved by examining their general structure. The overall structure of the smart grid environment is shown in Figure 1. Energy generated from diverse sources is transmitted over long distances through transmission lines and distributed to consumers via distribution lines. Data transmission is provided through AMI in the context of the energy infrastructure. While the Wide Area Network (WAN) is used in generation and transmission domains, the Neighborhood Area Network (NAN) and Field Area Network (FAN) are used in the distribution domain. Lastly, the Home Area Network (HAN) and Industrial Area Network (IAN) are used in the consumption domain.
Figure 1. Overall structure of the smart grid environment [20].
Energy theft detection in smart grids has been an active research area in recent years. The literature has introduced various strategies for detecting energy theft. These strategies include state estimation, game theory, and data-driven strategies. Data-driven strategies [21] are more prevalent due to their scalability for handling large systems and their cost-effectiveness in computational resources. Statistics, data mining, ML, and DL are among the prominent data-driven methods extensively employed to extract knowledge from consumption patterns, enabling inferential assessments. While detecting NTLs involves challenges, smart meters allow the extensive storage of energy data, enabling various analytical approaches. This has led to the development of various classification techniques.
Jokar et al. [22] propose an energy theft detector within AMI based on consumption patterns, utilizing the SVM approach. The detector enhances the classification accuracy to 94%. Moreover, it addresses a range of cyber-attack vectors associated with energy theft, and these are widely acknowledged in the literature. The authors of [23] introduced a two-step energy-theft-detection system utilizing DT and SVM, achieving an accuracy of 92.5%. However, there is no information on whether the dataset is balanced or imbalanced. The researchers in [24] present an energy-theft-detection method utilizing ensemble ML models. The concept behind the models involves combining various ML methodologies into a unified predictive model to increase DR and decrease the error rate. The results indicate that a bagging-type ensemble ML approach, which aggregates the outcomes of independent ML models in parallel through averaging, outperforms a boosting approach. However, when compared to other approaches, the recommended model has not demonstrated better success.
Despite the absence of a real dataset in [25], notable achievements in performance were attained through the application of a neural network. They achieved an overall DR of 93%. The authors of [26] have devised a novel approach for identifying and detecting energy theft within distribution systems, employing the multilayer perceptron artificial neural network (MP- ANN). They achieved a successful differentiation between malicious and honest users, averaging a detection rate of 93.4%. However, there is no information on whether the dataset is balanced or imbalanced. In [27], a hybrid deep neural network (DNN) approach is proposed. The gated recurrent unit (GRU) technique was used, which is an evolved variant of LSTM belonging to the category of recurrent neural networks (RNNs). The hybrid DNN combines CNN, GRU, and particle swarm optimization (PSO). However, when compared to other approaches, the recommended hybrid model has not quite demonstrated better accuracy, and the proposed model tends to overfit. The work referenced as [28] employed a deep RNN classifier using GRU to catch temporal correlations within individual customer load profiles, thereby introducing a detector with a DR reaching up to 93%. However, it is not clear whether the dataset is balanced or imbalanced. In [29], the authors present a CNN model to detect energy theft, utilizing the State Grid Corporation of China (SGCC) dataset. They illustrate energy consumption over four weeks for randomly selected honest and malicious consumers. Initially, consumption is displayed by dates and later by weeks. Date-based representation fails to differentiate between honest users and thieves, but the weekly representation distinguishes them. Honest consumers show periodic energy usage, while the thieves display less periodicity. However, there is no information on whether the dataset is balanced or imbalanced. The researchers in [30] presented a hybrid model on energy consumption patterns to detect energy theft with CNN and long short-term memory (LSTM), using the SGCC dataset. The CNN autonomously identified and categorized features, whereas the LSTM managed the sequential nature of the time-based data. The authors solved the imbalanced dataset problem by applying the synthetic minority over-sampling technique (SMOTE) method to augment the NTL class, equalizing it with honest customer counts. While achieving an 89% accuracy, the model demonstrated a lower DR of nearly 87%. Compared to other approaches, the recommended hybrid model has not demonstrated better accuracy. Adil et al. [31] used the CNN-LSTM approach on the SGCC dataset and achieved 87.9% accuracy. However, compared to other approaches, the proposed model is not very satisfactory. Kocaman and Tümen [32] introduced an LSTM classifier for identifying malicious customers. They utilize data selection, normalization, and weight updating as preprocessing steps. The LSTM classifier architecture comprises LSTM cells, dropout layers, 𝑅𝑒𝐿𝑢 activation functions, and a softmax classifier. Evaluation involves precision, accuracy, and recall metrics for assessing model performance. However, it is unclear how they resolved the issue of the imbalanced dataset.
The authors in [33] used the Irish Social Science Data Archive (ISSDA) dataset. They employed cluster-based algorithms, specifically the fuzzy Gustafson–Kessel and fuzzy c-means, achieving a 74.1% area under the curve (AUC). However, they achieved low true positive rate (TPR) and high FPR, which are 63.6% and 24.3%, respectively. Lastly, the authors of [34] describe an energy-theft-detection method using data about power provider system consumption at the edge. Centralized data centers employ K-means clustering and DNN to extract features. CNN refines daily, weekly, and monthly patterns. RF at the edge data center classifies the characteristics, speeding up the edge computing processing. This approach is more accurate and computationally efficient than previous methods, making it suitable for edge data centers.
Approaches using only traditional ML models often face challenges in extracting distinct consumption patterns due to the complex structure of power consumption data. This situation leads to low performance and accuracy. On the other hand, DL models can better explore complex structures, thus achieving higher success than ML models. Table 1 summarizes prominent ML- and DL-based approaches for developing energy theft detectors.
Table 1. Literature overview on energy theft detection based on consumption data.
Glancing at these noteworthy works, novel CNN-based hybrid models for energy theft detection and proposed a CNN-based deterministic model to detect energy theft based on consumption patterns were studied. CNN automatically captures the distinct features of consumption behaviors from the data. It is very important for the effectiveness of energy-theft-detection models. It was conducted that a comparative analysis using ML and sigmoid classifiers to detect consumption patterns based on extracted features, aiming to enhance detection performance. Hybrid solutions using both CNN and traditional ML methods have been observed to achieve higher TPR and lower FPR compared to pure DL solutions.


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