<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.</p>
</abstract>

A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection...

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Online veröffentlicht: 10. Nov. 2022

Seitenbereich: 73 - 90

Eingereicht: 03. Okt. 2022

Akzeptiert: 18. Okt. 2022

DOI: https://doi.org/10.2478/cait-2022-0040

Schlüsselwörter
Cybersecurity, Decision tree, Ensemble filter, Feature selection, Intelligent Water Drop, Intrusion Detection System

© 2022 Esra’a Alhenawi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Online veröffentlicht: 10. Nov. 2022

Seitenbereich: 73 - 90

Eingereicht: 03. Okt. 2022

Akzeptiert: 18. Okt. 2022

DOI: https://doi.org/10.2478/cait-2022-0040

SchlüsselwörterCybersecurity, Decision tree, Ensemble filter, Feature selection, Intelligent Water Drop, Intrusion Detection System

© 2022 Esra’a Alhenawi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Cybersecurity, Decision tree, Ensemble filter, Feature selection, Intelligent Water Drop, Intrusion Detection System