Machine Learning Algorithm for Detection of False Data Injection Attack in Power System
Ajit Kumar, Neetesh Saxena, Bong Jun Choi
Abstract
Electric grids are becoming smart due to the integration of Information and Communication Technology (ICT) with the traditional grid. However, it can also attract various kinds of Cyber-attacks to the grid infrastructure. The False Data Injection Attack (FDIA) is one of the lethal and most occurring attacks possible in both the physical and cyber part of the smart grid. This paper proposed an approach by applying machine learning algorithms to detect FDIAs in the power system. Several feature selection techniques are explored to investigate the most suitable features to achieve high accuracy. Various machine learning algorithms are tested to follow the most suitable method for building a detection system against such attacks. Also, the dataset has a skewed distribution between the two classes, and hence data imbalance issue is addressed during the experiments. Moreover, because the response time is critical in a smart grid, each experiment is also evaluated in terms of time complexity.