Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks

Nabil, Mahmoud; Ismail, Muhammad; Mahmoud, Mohamed; Shahin, Mostafa; Qaraqe, Khalid; Serpedin, Erchin

doi:10.1007/978-3-030-13057-2_4

Cited by 29 publications

(13 citation statements)

References 28 publications

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“…For DL-based classifiers, Nabil et al [22] introduced DL-based detectors to combat Cyberattacks targeting electricity theft in AMI networks within SGs. They developed both consumer-specific and generalized detectors utilizing deep feed-forward (FF) and recurrent neural networks (RNN).…”

Section: Supervised False Data Detectormentioning

confidence: 99%

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Habbak,

Mahmoud,

Fouda

et al. 2023

Energies

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In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing works in the literature assume attacks to compute malicious data. These detectors are trained to identify these attacks, but they cannot identify new attacks, which creates a vulnerability. Very few papers in the literature tried to address this problem by investigating anomaly detectors trained solely on benign data, but they suffer from these limitations: (1) low detection accuracy and high false alarm; (2) the need for knowledge on the malicious data to compute good detection thresholds; and (3) they cannot capture the temporal correlations of the readings and do not address the class overlapping issue caused by some deceptive attacks. To address these limitations, this paper presents a deep support vector data description (DSVDD) based unsupervised detector for false data in smart grid. Time-series readings are transformed into images, and the detector is exclusively trained on benign images. Our experimental results demonstrate the superior performance of our detectors compared to existing approaches in the literature. Specifically, our proposed DSVDD-based schemes have exhibited improvements of 0.5% to 3% in terms of recall and 3% to 9% in terms of the Area Under the Curve (AUC) when compared to existing state-of-the-art detectors.

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Section: Supervised False Data Detectormentioning

confidence: 99%

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Habbak,

Mahmoud,

Fouda

et al. 2023

Energies

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“…The research published in [45] used an RNN classifier based on GRU that captures the temporal correlation in the customer's load profile. DL based classifiers include feed-forward neural networks [46], RNNs [45], and vector embedding [47], were utilized as ETD.…”

Section: A Electricity Theft In Consumption Domainmentioning

confidence: 99%

Fine-Tuned RNN-Based Detector for Electricity Theft Attacks in Smart Grid Generation Domain

Eddin

Albaseer

Abdallah

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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In this paper, we investigate the problem of electricity theft attacks on smart meters when malicious customers (i.e., adversaries) claim injecting more generated energy into the grid to get more profits from utility companies. These attacks can be applied by accessing the smart meters monitoring renewable-based distributed generation (DG), and manipulating the reading. In this paper, we propose approaches that: (1) rely on data sources with only a single generator (i.e., solar only) and multi-fuel type and (2) address the crucial effects of slight perturbations that the attacker can add, which can deceive the detector. In particular, this paper introduces an efficient multi-task DL-based detector that offers a higher detection rate, copes with different fuel types, and uses only single data sources. The proposed detector incorporates months and days as two additional features to boost the performance and properly guide the model to successful detection. The proposed method is then extended to consider small perturbations that attackers may use to apply successful attacks. We conduct extensive simulations for two different detectors, one for solar DG and the other for multiple fuel types (i.e., solar and wind).Using a realistic dataset, the results reveal that the proposed RNN-based detectors identify adversaries at a higher rate than the existing solutions, even with minimal perturbations and different fuel types.

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“…where x i is the amount of electricity consumption in the specified period, and NaN indicates empty or non-numerical values. In the next step, outliers are reset using Equation (2).…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Non-technical losses can lead to increased network load, loss of utility company revenue and threats such as fire and electric shock [1]. The amount of annual loss in the United States, India, United Kingdom and Canada is around $6 billion, $17 billion, $173 million and $100 million respectively [2].…”

Section: Introductionmentioning

confidence: 99%

On the effect of sampling frequency on the electricity theft detection performance

Nasab

Ghaderi

2022

IET Signal Processing

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Electricity theft is a common problem in many countries and affects the revenue of the electricity industry significantly. Recently, machine and deep learning techniques are being used widely to detect thieves by analysing the consumption patterns. While the prediction accuracy of these methods depends on the number and quality of the existing samples used for training models, the majority of previous research work focussed on data with high sampling frequencies, for example, data from smart grids. However, based on technological and operational limitations, the rate of metre reading is too low in many countries, which can affect detection performance. To investigate the effect of sampling frequency on the performance of detection methods, we designed a processing framework to evaluate different classification algorithms on versions of a challenging dataset obtained by down‐sampling the original data at various rates. We used different features from time, frequency and time‐frequency domains and their combinations in our investigations. Experimental results show that the performance of the models decreases with a small slope up to the sampling frequency of one data per week. The performance degradation rate is significant for data with lower sampling frequencies. We also proposed a bagging network that could improve the overall detection performance at different sampling frequencies.

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Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks

Cited by 29 publications

References 28 publications

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Fine-Tuned RNN-Based Detector for Electricity Theft Attacks in Smart Grid Generation Domain

On the effect of sampling frequency on the electricity theft detection performance

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