Detection and Localization of False Data Injection Attacks in Smart Grids Applying an Interpretable Fuzzy Genetic Machine Learning/Data Mining Approach

Detection and Localization of False Data Injection Attacks in Smart Grids Applying an Interpretable Fuzzy Genetic Machine Learning/Data Mining Approach

In this paper, we consider the problem of accurate, transparent, and interpretable detection, as well as the localization of false data injection attacks (FDIAs) in smart grids. In order to address that problem, we employ our knowledge discovery machine learning/data mining (ML/DM) approach—implemen...

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Bibliographic Details
Main Authors: Marian B. Gorzałczany, Filip Rudziński
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Energies
Subjects:
false data injection attacks (FDIAs)
machine learning (ML)
data mining (DM)
fuzzy rule-based classifiers
interpretable FDIA detection and localization
multiobjective evolutionary optimization
Online Access:https://www.mdpi.com/1996-1073/18/7/1568
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