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

Eddin, Maymouna Ez; Albaseer, Abdullatif; Abdallah, Mohamed; Bayhan, Sertaç; Qaraqe, Marwa; Al-Kuwari, Saif; Abu‐Rub, Haitham

doi:10.1109/ojies.2022.3224784

Cited by 12 publications

(3 citation statements)

References 55 publications

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“…This integration significantly enhances various aspects, particularly in ensuring safety, where dedicated efforts combat cyber attacks. For instance, Eddin [29] proposes an efficient multitask deep learningbased detector aimed at thwarting electricity theft attacks on smart meters. Similarly, Keliris et al [30] introduce a novel Machine Learning (ML)-based Intrusion Detection System tailored to combat cyber attacks on smart grids.…”

Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks

Samarajeewa,

De Silva,

Manic

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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Crowd monitoring is a primary function in diverse industrial domains, such as smart cities, public transport, and public safety. Recent advancements in low-energy devices and rapid connectivity have enabled the generation of real-time data streams suitable for crowd-monitoring applications. Crowd forecasting is typically achieved using deep learning models that learn the evolving nature of data streams. The computational complexity, execution time and opaqueness are inherent challenges of deep learning models that also overlook the latent relationships between multiple real-time data streams for improved accuracy. To address these challenges, we propose the Global Ensemble Echo State Network approach for explainable crowd forecasting using multiple WiFi data streams. This approach replaces the random input mapping layer with a clustering layer, allowing the network to learn input projections on cluster centroids. It incorporates an ensemble readout comprising a stack of reservoir layers that provide model explainability. It also learns multiple related time series in parallel to construct a global model that leverage latent relationships across the data streams. This approach was empirically evaluated in a multi-campus, mixed-use tertiary education setting. The results of which confirm the effectiveness and interpretability of the proposed approach for industrial applications of crowd forecasting.

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Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks

Samarajeewa,

De Silva,

Manic

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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“…Machine learning (ML), as a rapidly growing technology, can play a crucial role in detecting cyber attacks on SGs [2,9,10,11]. ML algorithms can be trained to identify patterns and anomalies in large volumes of data generated by the grid using either anomaly-based or signature-based detection methods [12].…”

Section: Introductionmentioning

confidence: 99%

Double-Edged Defense: Thwarting Cyber Attacks and Adversarial Machine Learning in IEC 60870-5-104 Smart Grids

Teryak,

Albaseer,

Abdallah

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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Smart grids (SGs), a cornerstone of modern power systems, facilitate efficient management and distribution of electricity. Despite their advantages, increased connectivity and reliance on communication networks expand their susceptibility to cyber threats. Machine learning (ML) can radically transform cybersecurity in SGs and secure protocols as in IEC 60870 standard, an international standard for electric power system communication. Notwithstanding, cyber adversaries are now exploiting ML-based intrusion detection systems (IDS) using adversarial ML attacks, potentially undermining SG security. This paper addresses cyber attacks on the communication network of SGs, specifically targeting the IEC 60870-5-104 protocol. We introduce a novel ML-based IDS framework for the IEC 60870-5-104 protocol. Specifically, we employ an artificial neural network (ANN) to analyze a new and realistically representative dataset of IEC 60870-5-104 traffic data, unlike previous research that relies on simulated or unrelated data. This approach assists in identifying anomalies indicative of cyber attacks more accurately. Furthermore, we evaluate the resilience of our ANN model against adversarial attacks, including the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini and Wagner (C&W) attacks. Our results demonstrate that the proposed framework can accurately detect cyberattacks and remains robust to adversarial attacks. This offers efficient and resilient IDS capabilities to detect and mitigate cyber-attacks in real-world ML-based adversarial environments.

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“…Combining artificial intelligence and group intelligence in intrusion detection systems reduces their false alarm rate while detecting attacks. Deep learning processes, including Long-and Short-term Memory (LSTM) [18], Convolutional Neural Network (CNN) [17], and Recurrent Neural Network (RNN) [19], are effective in detecting Smart grid attacks. However, their error rate can be significant.…”

Section: Introductionmentioning

confidence: 99%

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

Mhmood,

Ergül,

Rahebi

2023

SIViP

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Cyber attacks against Smart Grids (SG) have harmful effects. The first function of a defensive system is to provide an intelligent system to detect intrusions. The nature of attacks against smart grids is very complex, so the intrusion detection system must be able to detect complex attacks. Lack of balancing and optimization of deep learning methods are the main challenges for many intrusion detection systems. This research presents an intelligent intrusion detection system for a smart grid based on Game Theory, Swarm Intelligence, and Deep Learning (DL). First, the proposed method balances the training samples with a conditional DL technique based on Game Theory and CGAN. Secondly, the Aquila Optimizer (AO) algorithm selects features. The third step involves mapping the selected features on the dataset and coding reduced-dimension samples into RGB color images, which are used to train the VGG19 neural network. In the fourth step, the AO algorithm optimally adjusts meta-parameters to reduce the error of the VGG19 neural network. Tests performed on the NSL-KDD dataset show that the proposed method's accuracy, sensitivity, and precision in detecting attacks are 99.82%, 99.69%, and 99.76%, respectively. The CGAN method balances the dataset and increases the accuracy, sensitivity, and precision of the proposed method compared to the GAN method in detecting attacks on the smart grid. Experiments show that the proposed method more accurately detects attacks than deep learning methods such as VGG19, CNN-GRU, CNN-GRU-FL, LSTM, and CNN.

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

Cited by 12 publications

References 55 publications

Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks

Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks

Double-Edged Defense: Thwarting Cyber Attacks and Adversarial Machine Learning in IEC 60870-5-104 Smart Grids

Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm

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