PSO-Driven Feature Selection and Hybrid Ensemble for Network Anomaly Detection

Louk, Maya Hilda Lestari and Tama, Bayu Adhi (2022) PSO-Driven Feature Selection and Hybrid Ensemble for Network Anomaly Detection. Big Data and Cognitive Computing, 6 (4). 137/1-13. ISSN 2504-2289

PDF PSO-Driven Feature Selection and Hybrid Ensemble.pdf Download (366kB)

Abstract

As a system capable of monitoring and evaluating illegitimate network access, an intrusion detection system (IDS) profoundly impacts information security research. Since machine learning techniques constitute the backbone of IDS, it has been challenging to develop an accurate detection mechanism. This study aims to enhance the detection performance of IDS by using a particle swarm optimization (PSO)-driven feature selection approach and hybrid ensemble. Specifically, the final feature subsets derived from different IDS datasets, i.e., NSL-KDD, UNSW-NB15, and CICIDS-2017, are trained using a hybrid ensemble, comprising two well-known ensemble learners, i.e., gradient boosting machine (GBM) and bootstrap aggregation (bagging). Instead of training GBM with individual ensemble learning, we train GBM on a subsample of each intrusion dataset and combine the final class prediction using majority voting. Our proposed scheme led to pivotal refinements over existing baselines, such as TSE-IDS, voting ensembles, weighted majority voting, and other individual ensemble-based IDS such as LightGBM

Item Type:	Article
Uncontrolled Keywords:	multi-stage ensemble; particle swarm optimization; feature selection; anomaly detection; intrusion detection
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Ester Sri W. 196039
Date Deposited:	14 Nov 2022 07:58
Last Modified:	14 Nov 2022 07:58
URI:	http://repository.ubaya.ac.id/id/eprint/42863

Actions (login required)

View Item