Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Arif, Arooj; Javaid, Nadeem; Aldegheishem, Abdulaziz; Alrajeh, Nabil

doi:10.1002/cpe.6316

Cited by 25 publications

(12 citation statements)

References 44 publications

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“…The results achieved 61% accuracy, 96% recall, and 64% f-measure. Arif et al [21] proposed a detection model on a State Grid Corporation of China (SGCC) dataset [22]. They solve the imbalanced data using a hybrid resampling technique named synthetic minority oversampling technique with a near miss.…”

Section: Machine Learning Solutionsmentioning

confidence: 99%

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Almarshdi¹,

Nassef²,

Fadel³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning algorithms. We implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that categorizes the network traffic into normal and attacks traffic. In this work, interpolation data preprocessing is used to compute the missing values. Also, the imbalanced data problem is solved using a synthetic data generation method. Extensive experiments have been implemented to compare the performance results of the proposed model (CNN+LSTM) with a basic model (CNN only) using both balanced and imbalanced dataset. Also, with some state-of-the-art machine learning classifiers (Decision Tree (DT) and Random Forest (RF)) using both balanced and imbalanced dataset. The results proved the impact of the balancing technique. The proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy (92.10%) compared with the basic CNN model (89.90%) and the machine learning (DT 88.57% and RF 90.85%) models. Moreover, comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

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Section: Machine Learning Solutionsmentioning

confidence: 99%

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Almarshdi¹,

Nassef²,

Fadel³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…In [31], the authors considered the imbalance data problem, high dimensionality issue, and classification. SMOTE and near miss (NM) are used in combination to resolve the data imbalance issue.…”

Section: Related Workmentioning

confidence: 99%

“…In our scenario, 0 means that the consumer is honest while 1 means that the consumer is dishonest or theft. This technique is also used by many researchers in the literature for ETD, which is a binary classification problem [12], [31], [43], etc.…”

Section: ) Logistic Regressionmentioning

confidence: 99%

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

et al. 2022

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Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

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“…Because minority class representation in the test sample is restricted or sometimes it doesn't appear, the overall accuracy may still be high, but the memory of the minority class is low. This shows that a model fails to recognize the class of minorities [10]. Because many machine learning problems are inherently unbalanced, this problem may happen most of the time [11].…”

Section: Introductionmentioning

confidence: 99%

Data Generative Model to Detect the Anomalies for IDS Imbalance CICIDS2017 Dataset

et al. 2023

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The system of intrusion detection dataset enables machine learning to recognize attack activity in the network. The intrusion, however, is naturally imbalanced, most of the traffic is normal traffic. Moreover, a certain attack is more popular than others. Therefore, the number of cases is highly imbalanced with the majority of attacks dominated by Distributed Denial of Services (DDoS), Denial of Service Hulk (DoS_Hulk), and PortScan more than 4.5% of attacks data. While the minority attack such as DoS_goldeneye, DoS_slowloris, DoS_slowhttptest, Web Attacks, Infiltration, Bot, and Heartbleed was only recorded in less than 1% of attack data. We propose data generative model (DGM) using the Conditional Generative Adversarial Network (CGAN) to improve the class of minorities of the IDS dataset. In this study, we tested the performance of the Data Generative Model based on CGAN in the CICIDS2017 dataset. There are new attacks in this dataset, including Bot, Web_attacks, Infiltration and Heartbleed. According to our experiments, the model successfully detect new attacks and improves the weighted f1-score by 99,92% compared to that of achievers by existing methods using the CICIDS2017 dataset.

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Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Cited by 25 publications

References 44 publications

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

Data Generative Model to Detect the Anomalies for IDS Imbalance CICIDS2017 Dataset

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