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Open Access

Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia, and also with University Center for Research and Development (UCRD), Department of Computer Science and Engineering, Chandigarh University, Punjab, India
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Abstract

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

Keywords

feature selectionintrusion detection systemsoftware-defined networkdecision treerandom forestlogistic regressionXGB classifierAdaBoost

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Tsinghua Science and Technology
Volume 29 Issue 4,
August 2024
Pages 948-958
DOI: 10.26599/TST.2023.9010032
Cite this article:
Eljialy AEM, Uddin MY, Ahmad S. Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning. Tsinghua Science and Technology, 2024, 29(4): 948-958. https://doi.org/10.26599/TST.2023.9010032

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Received: 06 March 2023
Revised: 10 April 2023
Accepted: 14 April 2023
Published: 09 February 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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