Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into comm...

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Bibliographic Details
Published in:Energies (Basel) 2022-01, Vol.15 (1), p.212
Main Authors: Kumar, Ajit, Saxena, Neetesh, Jung, Souhwan, Choi, Bong Jun
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Classification
Communication
critical infrastructure
Data Injection Attack
Datasets
Decision making
Electricity distribution
Feature selection
Infrastructure
Injection
Learning algorithms
Machine learning
Neural networks
power system
smart grid
Water treatment
water treatment plant
Water treatment plants
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Summary:Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15010212