Version 1
: Received: 28 June 2021 / Approved: 29 June 2021 / Online: 29 June 2021 (13:30:30 CEST)
How to cite:
Kasongo, S. M. Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection. Preprints2021, 2021060710. https://doi.org/10.20944/preprints202106.0710.v1
Kasongo, S. M. Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection. Preprints 2021, 2021060710. https://doi.org/10.20944/preprints202106.0710.v1
Kasongo, S. M. Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection. Preprints2021, 2021060710. https://doi.org/10.20944/preprints202106.0710.v1
APA Style
Kasongo, S. M. (2021). Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection. Preprints. https://doi.org/10.20944/preprints202106.0710.v1
Chicago/Turabian Style
Kasongo, S. M. 2021 "Genetic Algorithm Based Feature Selection Technique for Optimal Intrusion Detection" Preprints. https://doi.org/10.20944/preprints202106.0710.v1
Abstract
In recent years, several industries have registered an impressive improvement in technological advances such as Internet of Things (IoT), e-commerce, vehicular networks, etc. These advances have sparked an increase in the volume of information that gets transmitted from different nodes of a computer network (CN). As a result, it is crucial to safeguard CNs against security threats and intrusions that can compromise the integrity of those systems. In this paper, we propose a machine mearning (ML) intrusion detection system (IDS) in conjunction with the Genetic Algorithm (GA) for feature selection. To assess the effectiveness of the proposed framework, we use the NSL-KDD dataset. Furthermore, we consider the following ML methods in the modelling process: decision tree (DT), support vector machine (SVM), random forest (RF), extra-trees (ET), extreme gradient boosting (XGB), and naïve Bayes (NB). The results demonstrated that using the GA algorithm has a positive impact on the performance of the selected classifiers. Moreover, the results obtained by the proposed ML methods were superior to existing methodologies.
Keywords
Intrusion Detection; Genetic Algorithm
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
