False Data Injection Attacks Detection in Power System Using Machine Learning Method
Journal of Computer and Communications
False data injection attacks (FIDAs) against state estimation in power system are a problem that could not be effectively solved by traditional methods. In this paper, we use four outlier detection methods, namely one-Class SVM, Robust covariance, Isolation forest and Local outlier factor method from machine learning area in IEEE14 simulation platform for test and compare their performance. The accuracy and precision were estimated through simulation to observe the classification effect.
... ction As an important role of the country, the power system has a vital impact on the national economy and public safety. With the in-depth application of information and communication technologies in modern power systems, power systems are gradually developing into cyber-physical systems (CPS) that are integrated by power physical networks and information networks. In smart grids, it requires high quality interaction between the information system and the physical system. However, due to the inevitable defects and loopholes in information communication systems in the power system, data collection, information transmission, and even data control centers are at risk of being attacked, resulting in security incidents in the power network . State estimation in power system is the estimation of the current system state, to provide data support for the EMS (Energy Management System) to do the op-How to cite this paper: