Detection of False Data Injection Attacks (FDIA) Targeting Distribution Transformers in Smart Grids Using Hybrid Artificial Intelligence Methods
JOURNAL OF INFORMATION ASSURANCE AND SECURITY, cilt.21, sa.4, ss.217-229, 2026 (ESCI)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.2478/ias-2026-0011
- Dergi Adı: JOURNAL OF INFORMATION ASSURANCE AND SECURITY
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI)
- Sayfa Sayıları: ss.217-229
- Manisa Celal Bayar Üniversitesi Adresli: Evet
Özet
Communication technologies in modern power systems have rendered energy grids vulnerable to False Data Injection Attacks (FDIA). This study evaluates FDIA detection on the IEEE 118-bus test system using pandapower. A synthetic dataset of 2000 samples was partitioned into 75%/25% training/test splits. Three models - LSTM networks, Isolation Forest and One-Class SVM - were comparatively assessed. LSTM achieved the highest performance with 97.0% recall, F1-score of 0.965 and AUC of 0.987, outperforming Isolation Forest (93.0%) and One-Class SVM (90.0%). These results confirm that deep learning approaches are highly effective for smart grid cybersecurity and compatible with IEC 61850-based infrastructures.