Robust Data-Driven Detection of Electricity Theft Adversarial Evasion Attacks in Smart Grids

Takiddin, Abdulrahman; Ismail, Muhammad; Serpedin, Erchin

doi:10.1109/tsg.2022.3193989

Cited by 31 publications

(6 citation statements)

References 39 publications

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“…To further enhance the anomaly detection, LOF technique is used to compute an anomaly score for each identified outlier candidate, providing a comprehensive assessment of their anomalous behavior. However, it should be noted that statistical and analytical methods often suffer from limitations in capturing the temporal dynamics and intricate patterns present in the data, which may degrade their accuracy [40].…”

Section: ) Statistical and Analytical-based Methodsmentioning

confidence: 99%

Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

El-Toukhy,

Mahmoud,

Bondok

et al. 2023

IEEE Access

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Fraudulent customers in smart power grids employ cyber-attacks by manipulating their smart meters and reporting false consumption readings to reduce their bills. To combat these attacks and mitigate financial losses, various machine learning-based electricity theft detectors have been proposed. Unfortunately, these detectors are vulnerable to serious cyber-attacks, specifically evasion attacks. The objective of this paper is to investigate the robustness of deep reinforcement learning (DRL)-based detectors against our proposed evasion attacks through a series of experiments. Firstly, we introduce DRL-based electricity theft detectors implemented using the double deep Q networks (DDQN) algorithm. Secondly, we propose a DRL-based attack model to generate adversarial evasion attacks in a black box attack scenario. These evasion samples are generated by modifying malicious reading samples to deceive the detectors and make them appear as benign samples. We leverage the attractive features of reinforcement learning (RL) to determine optimal actions for modifying the malicious samples. Our DRL-based evasion attack model is compared with an FGSM-based evasion attack model. The experimental results reveal a significant degradation in detector performance due to the DRL-based evasion attack, achieving an attack success rate (ASR) ranging from 92.92% to 99.96%. Thirdly, to counter these attacks and enhance detection robustness, we propose hardened DRL-based defense detectors using an adversarial training process. This process involves retraining the DRL-based detectors on the generated evasion samples. The proposed defense model achieves outstanding detection performance, with a degradation in ASR ranging from 1.80% to 9.20%. Finally, we address the challenge of whether the DRL-based hardened defense model, which has been adversarially trained on DRL-based evasion samples, is capable of defending against FGSM-based evasion samples, and vice versa. We conduct extensive experiments to validate the effectiveness of our proposed attack and defense models. INDEX TERMSSecurity, electricity theft, evasion attacks, reinforcement learning, adversarial training, and smart power grids.

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Section: ) Statistical and Analytical-based Methodsmentioning

confidence: 99%

Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

El-Toukhy,

Mahmoud,

Bondok

et al. 2023

IEEE Access

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“…Takiddin et al [36][37][38][39][40] utilized multiple deep autoencoder anomaly detectors to detect electricity theft. The results indicate that deep architectures outperform shallow detectors in terms of detection performance and the recurrent LSTM-based architectures could further enhance the detection performance compared to static fully connected detectors.…”

Section: Unsupervised False Data Detectormentioning

confidence: 99%

“…Conversely, the unsupervised detectors are trained exclusively on benign samples and subsequently tested on datasets comprising both benign and malicious instances. This category includes shallow models such as OC-SVM [16] and ARIMA [31], alongside a variety of deep autoencoders [36][37][38][39][40].…”

Section: Benchmark Detectorsmentioning

confidence: 99%

“…The dynamic ARIMA model can capture temporal dependencies but it has a shallow architecture that fails to detect data overlap adequately. Moreover, the autoencoders introduced in [36][37][38][39][40] assume the existence of malicious data and use it to optimize the threshold of the detector. This dependency on specific malicious data limits the detector's practicality in detecting new attacks, particularly deceptive attacks that cause data overlap.…”

Section: Benchmark Detectorsmentioning

confidence: 99%

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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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“…Takiddin et al [33] have proposed using an anomaly detector trained only on benign data to identify both traditional and EAs for electricity theft. They have achieved this by sequentially combining multiple neural network architectures, namely an autoencoder, convolutional-recurrent network, and feed-forward neural network.…”

Section: B Evasion Attacks Detection Schemesmentioning

confidence: 99%

Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning

Bondok,

Mahmoud,

Badr

et al. 2023

IEEE Access

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In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks (EAs). EAs aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers' detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to EAs for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against EAs, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce three novel attacks, namely Distillation, No-Adversarial-Sample-Training, and False-Labeling, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of these proposed attacks. Our findings reveal that the AT can counter EAs effectively when the FL participants are honest, but it fails when they act maliciously and launch our attacks. This works lays the foundation for future endeavors in exploring additional countermeasures, in conjunction with AT, to bolster the security and resilience of FL machine learning models against adversarial attacks in the context of electricity theft detection.INDEX TERMS Security, Evasion attacks, Federated learning, and Smart power grid.

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Robust Data-Driven Detection of Electricity Theft Adversarial Evasion Attacks in Smart Grids

Cited by 31 publications

References 39 publications

Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

Countering Evasion Attacks for Smart Grid Reinforcement Learning-Based Detectors

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

Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning

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