Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning


Stealthy false data injection attacks target state estimation in energy management systems in smart power grids to adversely affect operations of the power transmission systems. This paper presents a data-driven machine learning based scheme to detect stealthy false data injection attacks on state estimation. The scheme employs ensemble learning, where multiple classifiers are used and decisions by individual classifiers are further classified. Two ensembles are used in this scheme, one uses supervised classifiers while the other uses unsupervised classifiers. The scheme is validated using simulated data on the standard IEEE 14-bus system. Experimental results show that the performance of both supervised individual and ensemble models are comparable. However, for unsupervised models, the ensembles performed better than the individual classifiers.

Publication Title

Computers and Security