Detection of False Stealthy Data Injection Attacks in Smart Meters Using Machine Learning and Blockchain Technology

Abstract

Despite the benefits associated with the use of smart meters in advanced metering infrastructure, the widespread deployment of such meters has introduced vulnerabilities that leave power systems susceptible to stealthy false data injection attacks which cannot be detected by conventional methods. In this paper, we present a framework that combines a bidirectional long short-term memory network with an attention mechanism and a blockchain integrity layer to provide secure anomaly detection. We utilize the Smart Meter Electricity Consumption Dataset, augmented with synthetically injected anomalies, to detect abnormal consumption behavior. Experimental results demonstrate that the proposed hybrid model achieves up to 96.23% accuracy, 99.88% precision, 92.58% recall, and 96.09% F1-score, outperforming eXtreme Gradient Boosting (XGBoost), Isolation Forest, and Random Forest baselines. Confusion matrix analysis confirms minimal false positives and strong detection capability for stealthy attacks. The blockchain layer ensures immutability and trustworthiness of detection results through cryptographic hashing and consensus mechanisms with negligible overhead. The proposed framework offers a scalable, secure, and interpretable solution for defending smart grid infrastructures against complex cyberattacks.