A Unified Cybersecurity Framework for Smart Grids Against Data Integrity Attacks Using Ensemble Learning and Hybrid Encryption
Year: 2025
Type: article
Abstract: The increasing frequency and sophistication of cyberattacks on smart grid infrastructures have raised critical concerns over data integrity, operational resilience, and real-time response capabilities. This study introduces a unified cybersecurity framework for cyber-physical power systems that integrate high-performance anomaly detection with provably secure cryptographic protection. A comprehensive dataset, built upon the IEEE 24-bus test system, includes a diverse set of operational states and five classes of false data injection attacks (FDIAs), including stealthy and replay-based intrusions. To accurately detect both common and sophisticated threats, we implement a suite of supervised learning models—RF, MLP, and Decision Trees—alongside an ensemble strategy termed MVCC, which achieves up to 99.90% accuracy in binary classification and 99.88% in multiclass settings. For defense at the data level, we deploy a two-tier encryption architecture combining AES-GCM (for confidentiality and authenticity) with RSA-OAEP (for secure key management), demonstrating strong resilience against standard attack models (COA, KPA, CPA, CCA) and achieving nearly uniform ciphertext entropy (7.99 bits/byte). The system’s real-time applicability is validated through the deployment of the RF classifier on a PYNQ-Z2 FPGA platform, attaining sub-second inference latency. Further, unsupervised (DBSCAN, K-Means) and temporal (LSTM) models are incorporated for stealthy anomaly localization and early threat prediction. This work presents a scalable, interpretable, and cryptographically secure solution for protecting next-generation smart grids against evolving data integrity threats.
Source: IEEE Access
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4414693253