Securing the grid: A comprehensive analysis of cybersecurity challenges in PMU-based cyber-physical power networks

Electric Power Systems Research, Год журнала: 2024, Номер 233, С. 110509 - 110509

Опубликована: Май 25, 2024

Язык: Английский

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HGCNN-LSTM: A Data-driven Approach for Cyberattack Detection in Cyber-Physical Systems

SN Computer Science, Год журнала: 2025, Номер 6(1)

Опубликована: Янв. 7, 2025

Язык: Английский

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Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis Classification

Опубликована: Янв. 1, 2024

Язык: Английский

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Advancements in Grid Resilience: Recent Innovations in AI-Driven Solutions

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105042 - 105042

Опубликована: Апрель 1, 2025

Язык: Английский

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Digital twins for sustainable design and management of smart city buildings and municipal infrastructure

Sustainable Energy Technologies and Assessments, Год журнала: 2024, Номер 64, С. 103682 - 103682

Опубликована: Фев. 29, 2024

Язык: Английский

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Multi-Behavior Contrastive Learning with graph neural networks for recommendation

Knowledge-Based Systems, Год журнала: 2024, Номер 301, С. 112211 - 112211

Опубликована: Июль 14, 2024

Язык: Английский

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Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems

Applied Sciences, Год журнала: 2024, Номер 14(16), С. 6865 - 6865

Опубликована: Авг. 6, 2024

As the informatization of power systems advances, secure operation faces various potential network attacks and threats. The false data injection attack (FDIA) is a common mode that can lead to abnormal system operations serious economic losses by injecting into terminal links or devices. current research on FDIA primarily focuses detecting its existence, but there relatively little localization attacks. To address this challenge, study proposes novel method (GA-CNN-LSTM) combines convolutional neural networks (CNNs), long short-term memory (LSTM), genetic algorithm (GA) accurately locate attacked bus line. This utilizes CNN extract local features LSTM with time series information global features. It integrates deeply explore complex patterns dynamic changes in data, effectively optimize hyperparameters using GA ensure an optimal performance model. Simulation experiments were conducted IEEE 14-bus 118-bus test systems. results indicate GA-CNN-LSTM achieved F1 scores for location identification 99.71% 99.10%, respectively, demonstrating superior compared other methods.

Язык: Английский

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Stochastic Gradient Boosted Distributed Decision Trees Security Approach for Detecting Cyber Anomalies and Classifying Multiclass Cyber-Attacks

Computers & Security, Год журнала: 2025, Номер unknown, С. 104320 - 104320

Опубликована: Янв. 1, 2025

Язык: Английский

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A Novel ConvXGBoost Method for Detection and Identification of Cyberattacks on Grid-Connected Photovoltaic (PV) Inverter System

Computation, Год журнала: 2025, Номер 13(2), С. 33 - 33

Опубликована: Фев. 1, 2025

The integration of solar Photovoltaic (PV) systems into the AC grid poses stability challenges, especially with increasing inverter-based resources. For an efficient operation system, smart grid-forming inverters need to communicate Supervisory Control and Data Acquisition (SCADA) system. However, Internet-of-Things devices that SCADA make these vulnerable. Though many researchers proposed Artificial-Intelligence-based detection strategies, identification location attack is not considered by strategies. To overcome this drawback, paper proposes a novel Convolution extreme gradient boosting (ConvXGBoost) method for only detecting Denial Service (DoS) False Injection (FDI) attacks but also identifying component system was compromised. model compared existing Neural Network (CNN) decision tree (DT) Simulation results demonstrate effectiveness both PV fuel cell (PV-FC) systems. example, accuracy 99.25% 97.76% CNN 99.12% DT during DoS on Moreover, can detect identify faster than other models.

Язык: Английский

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Semi-supervised federated learning for collaborative security threat detection in control system for distributed power generation

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 148, С. 110374 - 110374

Опубликована: Март 5, 2025

Язык: Английский

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