  False Data Injection (FDI) attacks are a common form of Cyber-attack
targetting smart grids. Detection of stealthy FDI attacks is impossible by the
current bad data detection systems. Machine learning is one of the alternative
methods proposed to detect FDI attacks. This paper analyzes three various
supervised learning techniques, each to be used with three different feature
selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus,
and 118-bus systems for evaluation of versatility. Accuracy of the
classification is used as the main evaluation method for each detection
technique. Simulation study clarify the supervised learning combined with
heuristic FS methods result in an improved performance of the classification
algorithms for FDI attack detection.
