Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks

Louk, Maya Hilda Lestari and Tama, Bayu Adhi (2021) Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks. Big Data and Cognitive Computing, 5 (4). 72/1-11. ISSN 2504-2289

PDF Maya Hilda_Exploring Ensemble-Based Class.pdf Download (11MB)

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.

Item Type:	Article
Uncontrolled Keywords:	imbalanced data; cost-sensitive learning; ensemble-based models; intrusion detection; industrial control networks
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	MAYA HILDA LESTARI LOUK
Date Deposited:	07 Dec 2021 03:01
Last Modified:	27 Sep 2022 07:27
URI:	http://repository.ubaya.ac.id/id/eprint/40800

Actions (login required)

View Item