Real-Time Anomaly-Driven Cyber Resilience: An Adaptive Machine Learning-Based Defense Against False Data Injection Attacks in Smart Grids

Abstract

This work proposes a machine learning-driven adaptive framework for real-time detection and mitigation of FDIAs in critical smart grid infrastructure. The adaptive nature of the model addresses evolving False Data Injection Attacks and provides a more secure and viable method of securing critical smart grid infrastructure from the injection of false data attacks. The fast digital transformation of smart grid infrastructure has created cybersecurity vulnerabilities. Conventional detection models are challenged, and the requirement of a complex solution is required to handle evolving attacks on critical smart grid infrastructure. The technical contributions of this research include continuous update of the model based on the evolving attacks. The model can adapt without retraining from scratch. This model is therefore applicable in future implementations of smart grids, where such models can be adopted by countries who wish to implement smart cities and utility companies in developing countries.