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- W4229332108 abstract "One of the barrier against developing cyber-physical systems is cyber security challenges that can affect security, reliability and operability of systems. As the most extensive manmade system, the power systems suffer from different kinds of cyber-attacks, which can jeopardize different sections and tasks of the grid. In recent years machine learning models have manifested promising performance in various areas, including detecting cyber-attacks. In this research, we use machine learning models to detect and locate a particular type of cyber-attack called false data injection attack (FDIA). The main idea of this work revolves around feature construction from basic rules of power system and importance of this features in accuracy of attack detection. This work consists of two parts. Firstly, we construct four sets of features from power system rules such as KCL. Moreover, one novelty of this paper is in the utilization of consensus algorithms in feature construction. Secondly, we use constructed feature sets with different machine learning models to find the best machine learning model. We also use probabilistic neural networks (PNN) as a model that has not been paid enough attention in other researches but works better with our feature sets compared to other scholars proposed method. In addition, we review several FDIA detection papers to compare our results and prove the superiority of our mechanism." @default.
- W4229332108 created "2022-05-09" @default.
- W4229332108 creator A5005267786 @default.
- W4229332108 creator A5048814619 @default.
- W4229332108 date "2022-01-19" @default.
- W4229332108 modified "2023-09-27" @default.
- W4229332108 title "Feature Construction to Detect and Locate FDIAs Using PNN and SVM models" @default.
- W4229332108 doi "https://doi.org/10.1109/ipaps55380.2022.9763201" @default.
- W4229332108 hasPublicationYear "2022" @default.
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