Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

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

doi:10.1109/jsyst.2021.3136683

Cited by 82 publications

(44 citation statements)

References 27 publications

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“…As previous both categories of ETD techniques are highlyexpensive, therefore, many researchers have moved towards the third category that is data mining based ETD techniques [11], [20], [21], [22], [23], [32], [33], [34], [35], [36], [37], and [38], to handle the problem of electricity theft in SGs. Authors in [11] propose a deep model for electricity theft and non-theft consumers' classification in SGs.…”

Section: Related Workmentioning

confidence: 99%

“…However, bayesian optimizer is employed for hyperparameters' optimization, which requires more computational time to generate candidate solutions. Besides, in [20], the authors propose a deep stacked autoencoder with long short term memory (LSTM) based structure for detection of anomaly in EC readings. The deep autoencoder is considered to help in recognizing the complex patterns in data while LSTM is considered to capture long sequences of the long term time series EC data.…”

Section: Related Workmentioning

confidence: 99%

“…This data is useful for researchers to develop novel schemes for electricity price prediction [14], demand response management [15], energy scheduling for demand side management [16], [17], and demand side load forecasting [18]. In addition, recently, authors in [19], [20], [21], [22], [23], [24], and [25], prove that conducting data analysis task in SGs assist in detecting energy theft. However, following are some limitations in these ETD approaches.…”

Section: Introductionmentioning

confidence: 99%

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RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

et al. 2022

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Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduction, usage of the standalone (single) electricity theft detectors, etc. These issues lead the classification techniques to low accuracy, minimum precision, low F1 score, and overfitting problems. For these reasons, it is extremely crucial to design such a novel strategy that is capable to tackle these issues and yield outstanding ETD performance. In this article, electricity theft happening in SGs is detected using a novel ETD approach. The proposed approach comprises recursive feature elimination (RFE), k nearest neighbor oversampling (KNNOR), bidirectional long short term memory (BiLSTM), and logit boosting (LogitBoost) techniques. Furthermore, three BiLSTM networks and a LogitBoost model are combined to make a BiLSTM-LogitBoost stacking ensemble model. Data preprocessing and feature selection followed by data balancing and electricity theft classification are the four major stages of the model proposed for ETD. It is obvious from the simulations performed using state grid corporation of China (SGCC)'s electricity consumption (EC) data that our proposed model achieves 96.32% precision, 94.33% F1 score, and 89.45% accuracy, which are higher than all the benchmarks employed in this study.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

et al. 2022

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“…The one-class SVM is also a shallow static anomaly detector that is trained only on benign data, which is then tested on both benign and malicious samples. The F-SAE is a static deep detector that learns the behavioral patterns of benign samples throughout the reconstruction process and detects malicious samples based on their deviation from the benign ones [34].…”

Section: A Benchmark Detectorsmentioning

confidence: 99%

“…Autoencoders are types of anomaly detectors [34] that operate by learning the behavioral patterns of a (normal) class. The learned behavioral patterns of that class are then used to identify abnormal deviations from those learned patterns.…”

Section: B Autoencoder-based Anomaly Detectionmentioning

confidence: 99%

Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids

Takiddin

Rath

Ismail

et al. 2022

IEEE Systems Journal

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Cyber-physical systems like microgrids contain numerous attack surfaces in communication links, sensors, and actuators forms. Manipulating the communication links and sensors is done to inject anomalous data that can be transmitted through the cyber-layer along with the original data stream. The presence of malicious, anomalous data packets in the cyber-layer of a DC microgrid can create hindrances in fulfilling the control objectives, leading to voltage instability and affecting load dispatch patterns. Hence, detecting anomalous data is essential for the restoration of system stability. This paper answers two important research questions: Which datadriven detection scheme offers the best detection performance against stealth cyber-attacks in DC microgrids? What is the detection performance improvement when fusing two features (i.e., current and voltage data) for training compared with using a single feature (i.e., current)? Our investigations revealed that (i) adopting an unsupervised deep recurrent autoencoder anomaly detection scheme in DC microgrids offers superior detection performance compared with other benchmarks. The autoencoder is trained on benign data generated from a multi-source DC microgrid model. (ii) Fusing current and voltage data for training offers a 14.7% improvement. The efficacy of the results is verified using experimental data collected from a DC microgrid testbed when subjected to stealth cyber-attacks.

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Electricity theft detection for energy optimization using deep learning models

Pamir,

Javaid,

Javed

et al. 2023

Energy Science & Engineering

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The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes up a major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructure along with the upgradation of the traditional grids to the smart grids (SGs) has helped the electric utilities to collect the electricity consumption (EC) readings of consumers, which further empowers the machine learning (ML) algorithms to be exploited for efficient electricity theft detection (ETD). However, there are still some shortcomings, such as class imbalance, curse of dimensionality, and bypassing the automated tuning of hyperparameters in the existing ML‐based theft classification schemes that limit their performances. Therefore, it is essential to develop a novel approach to deal with these problems and efficiently detect electricity theft in SGs. Using the salp swarm algorithm (SSA), gate convolutional autoencoder (GCAE), and cost‐sensitive learning and long short‐term memory (CSLSTM), an effective ETD model named SSA–GCAE–CSLSTM is proposed in this work. Furthermore, a hybrid GCAE model is developed via the combination of gated recurrent unit and convolutional autoencoder. The proposed model comprises five submodules: (1) data preparation, (2) data balancing, (3) dimensionality reduction, (4) hyperparameters' optimization, and (5) electricity theft classification. The real‐time EC data provided by the state grid corporation of China are used for performance evaluations via extensive simulations. The proposed model is compared with two basic models, CSLSTM and GCAE–CSLSTM, along with seven benchmarks, support vector machine, decision tree, extra trees, random forest, adaptive boosting, extreme gradient boosting, and convolutional neural network. The results exhibit that SSA–GCAE–CSLSTM yields 99.45% precision, 95.93% F1 score, 92.25% accuracy, and 71.13% area under the receiver operating characteristic curve score, and surpasses the other models in terms of ETD.

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Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

Cited by 82 publications

References 27 publications

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids

Electricity theft detection for energy optimization using deep learning models

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