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

• Published in: Energies (Basel) Vol. 18; no. 7; p. 1568
• Main Authors: Gorzałczany, Marian B., Rudziński, Filip
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21.03.2025
• Subjects: Accuracy; Analysis; Data mining; data mining (DM); Deep learning; false data injection attacks (FDIAs); fuzzy rule-based classifiers; Genetic algorithms; Genetic research; interpretable FDIA detection and localization; Knowledge discovery; Localization; Machine learning; machine learning (ML); Methods; multiobjective evolutionary optimization; Neural networks; Optimization
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en18071568

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—implemented as a collection of fuzzy rule-based classifiers (FR-BCs)—characterized by a genetically optimized accuracy–interpretability trade-off. Our approach uses our generalization (showing better performance) of the well-known SPEA2 method to carry out the genetic learning and multiobjective optimization process. The main contribution of this work is designing—using our approach—a collection of fast, accurate, and interpretable FR-BCs for FDIA detection and localization from the recently published FDIA data that describe various aspects of FDIAs in smart grids. Our approach generates FDIAs’ detection and localization systems characterized by very high accuracy (97.8% and 99.5% for the IEEE 14-bus and 118-bus systems, respectively) and very high interpretability (on average, 4.6 and 3.8 simple fuzzy rules for earlier-mentioned systems, respectively, i.e., a few easy to comprehend fuzzy rules). The contribution of this paper also includes a comparative analysis of our approach and 12 alternative methods applied to the same FDIAs’ data. This analysis shows that our approach totally outperforms the alternative approaches in terms of transparency and interpretability of FDIA detection and localization decisions while remaining competitive or superior in terms of the accuracy of generated decisions.