Improving detection of false data injection attacks using machine learning with feature selection and oversampling

Kumar, Ajit, Saxena, Neetesh ORCID: https://orcid.org/0000-0002-6437-0807, Souhwan, Jung and Choi, Bong Jun 2022. Improving detection of false data injection attacks using machine learning with feature selection and oversampling. Electronics 15 (1) , 212. 10.3390/en15010212

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Abstract

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%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
Publisher:	MDPI
ISSN:	2079-9292
Date of First Compliant Deposit:	24 February 2022
Date of Acceptance:	27 December 2021
Last Modified:	13 May 2023 18:26
URI:	https://orca.cardiff.ac.uk/id/eprint/147754

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