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False Data Injection (FDI) attacks are a common form of Cyber-attacktargetting smart grids. Detection of stealthy FDI attacks is impossible by thecurrent bad data detection systems. Machine learning is one of the alternativemethods proposed to detect FDI attacks. This paper analyzes three varioussupervised learning techniques, each to be used with three different featureselection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus,and 118-bus systems for evaluation of versatility. Accuracy of theclassification is used as the main evaluation method for each detectiontechnique. Simulation study clarify the supervised learning combined withheuristic FS methods result in an improved performance of the classificationalgorithms for FDI attack detection.

Smart Grid Cyber Attacks Detection using Supervised Learning and Heuristic Feature Selection