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Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling
| Content Provider | MDPI |
|---|---|
| Author | Kumar, Ajit Saxena, Neetesh Jung, Souhwan Choi, Bong Jun |
| Copyright Year | 2021 |
| Description | 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%. |
| Starting Page | 212 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15010212 |
| Journal | Energies |
| Issue Number | 1 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-12-29 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Information and Library Science Data Injection Attack Machine Learning Critical Infrastructure Smart Grid Water Treatment Plant Power System |
| Content Type | Text |
| Resource Type | Article |
