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A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace Ensemble to Defend Against Adversarial AttacksNATHANIEL, D.   , SOOSAI, A. |
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Author keywords
computer networks, computer security, machine learning, firewalls, intrusion detection
References keywords
intrusion(19), detection(19), learning(15), systems(11), machine(11), network(10), adversarial(10), security(8), attacks(6), system(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2023-11-30
Volume 23, Issue 4, Year 2023, On page(s): 81 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.04009
Web of Science Accession Number: 001147490000004
SCOPUS ID: 85184477312
Abstract
In recent years, machine learning (ML) has had a significant influence on the discipline of computer security. In network security, intrusion detection systems increasingly employ machine learning techniques. Approaches based on machine learning have substantially improved the efficacy of intrusion detection. Adaptive adversaries who comprehend the underlying principles of ML techniques can initiate attacks against the classification engine of an intrusion detection system. Malicious actors exploit machine learning model vulnerabilities. Network security, specifically intrusion detection systems, requires the development of defensive strategies to combat this threat. The RF-RSE (Random Forest based Random Subspace Ensemble) and RF-RSE-AT (RF-RSE-Adversarial Training) methods are proposed as network intrusion detection systems to defend against adversarial attacks. The methodologies proposed are evaluated using the NSL-KDD dataset. The RF-RSE method demonstrates remarkable resistance to adversary attacks. The RF-RSE-AT method performs exceptionally well in correctly identifying network traffic classes when presented with adversarial attacks, and it maintains its accuracy even when no attack is present. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
