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Exploring ensemble-based class imbalance learners for intrusion detection in industrial control networks

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Title
Exploring ensemble-based class imbalance learners for intrusion detection in industrial control networks
Author(s)
Louk, Maya Hilda Lestari; Bayu Adhi Tama
Publication Date
2021-12-01
Journal
Big Data and Cognitive Computing, v.5, no.4, pp.1 - 11
Publisher
MDPI
Abstract
Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefits of ensemble-based models for identifying threats and attacks in a cyber-physical power grid. We provide a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, including cost-sensitive C4.5, roughly balanced bagging, random oversampling bagging, random undersampling bagging, synthetic minority oversampling bagging, random undersampling boosting, synthetic minority oversampling boosting, AdaC2, and EasyEnsemble. Each ensemble’s performance is tested against a range of benchmarked power system datasets utilizing balanced accuracy, Kappa statistics, and AUC metrics. Our findings demonstrate that EasyEnsemble outperformed significantly in comparison to its rivals across the board. Furthermore, undersampling and oversampling strategies were effective in a boosting-based ensemble but not in a bagging-based ensemble.
URI
https://pr.ibs.re.kr/handle/8788114/11017
DOI
10.3390/bdcc5040072
ISSN
2504-2289
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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