TY  - EJOU
AU  - Lin, Chenxin 
AU  - Zhou, Qun 
AU  - Wang, Zhan 
AU  - Fan, Ximing 
AU  - Xu, Yaochang 
AU  - Xu, Yijia 

TI  - Detection of False Data Injection Attacks: A Protected Federated Deep Learning Based on Encryption Mechanism
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 3
SN  - 1546-2226

AB  - False Data Injection Attack (FDIA), a disruptive cyber threat, is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems, leading to system unreliability, data integrity loss and operational vulnerability exposure. Given its widespread harm and impact, conducting in-depth research on FDIA detection is vitally important. This paper innovatively introduces a FDIA detection scheme: A Protected Federated Deep Learning (ProFed), which leverages Federated Averaging algorithm (FedAvg) as a foundational framework to fortify data security, harnesses pre-trained enhanced spatial-temporal graph neural networks (STGNN) to perform localized model training and integrates the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption system to secure sensitive information. Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics, with peak improvements reaching up to 35%.
KW  - Smart grid; FDIA; federated learning; STGNN; CKKS homomorphic encryption

DO  - 10.32604/cmc.2025.065496