Blockchain-Integrated Federated Learning Framework for Detecting False Data Injection Attacks in Power Systems With Homomorphic Encryption

False Data Injection Attacks (FDIAs) pose a substantial risk to the reliability and stability of Cyber-Physical Power Systems (CPPS). While federated learning (FL) has emerged as a promising approach to detect such attacks without exposing sensitive data, security concerns remain in FL, including un...

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Ngā kaituhi matua:	Firdous Kausar, Sajid Hussain, Karl Walker, Ayesha Imam
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	IEEE 2025-01-01
Rangatū:	IEEE Open Access Journal of Power and Energy
Ngā marau:	Cyber-physical systems false data injection attack blockchain federated learning homomorphic encryption XGBoost
Urunga tuihono:	https://ieeexplore.ieee.org/document/11237138/
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!