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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2023 - 9

A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace Ensemble to Defend Against Adversarial Attacks

NATHANIEL, D. See more information about NATHANIEL, D. on SCOPUS See more information about NATHANIEL, D. on IEEExplore See more information about NATHANIEL, D. on Web of Science, SOOSAI, A. See more information about SOOSAI, A. on SCOPUS See more information about SOOSAI, A. on SCOPUS See more information about SOOSAI, A. on Web of Science
 
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Download PDF pdficon (1,073 KB) | Citation | Downloads: 558 | Views: 387

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
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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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References Weight

Web of Science® Citations for all references: 21,214 TCR
SCOPUS® Citations for all references: 40,513 TCR

Web of Science® Average Citations per reference: 684 ACR
SCOPUS® Average Citations per reference: 1,307 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-15 09:45 in 202 seconds.




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