False data injection attack (FDIA) is a major threat in wide-area monitoring systems. Being able to differentiate FDIA from normal grid contingencies is a paramount necessity for a grid operator to decide the correct response on a critical prompt basis as well as reduce the overall FDIAs false alarms. Two FDIAs characterization algorithms are developed in this paper. The first is based on the principal component analysis (PCA) while the second is based on the canonical correlation analysis (CCA). Both algorithms are developed in an online platform to reduce the computational complexity. The various designed test cases illustrate a promising FDIA characterization performance using both algorithms. The testing results of three machine learning-based classifiers indicate that the proposed FDIAs characterization algorithms provide better classification models than conventional PCA-based characterization algorithm with CCA illustrating advanced characterization and detection results.