Melola Discovery

← Melola Discovery

Jurnal SINTA 2

An Efficient Intrusion Detection System to Combat Cyber Threats using a Deep Neural Network Model

Journal of ICT Research and Applications (JICTRA) · Vol. 17 No. 3 (2023) · pp. 292–315

DOI
10.5614/itbj.ict.res.appl.2023.17.3.2
ISSN
2338-5499, 2337-5787
Published
2023-12-31
Publisher
DRI - ITB
Language
eng

Abstract

The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today’s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool.

Keywords

Collaboration network of the authors

5 authors · 10 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Vellore Institute of Technology University
  • Taibah University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">An Efficient Intrusion Detection System to Combat Cyber Threats using a Deep Neural Network Model</dc:title>
	<dc:creator>Ramaiah, Mangayarkarasi</dc:creator>
	<dc:creator>Vanmathi, C.</dc:creator>
	<dc:creator>Khan, Mohammad Zubair</dc:creator>
	<dc:creator>Vanitha, M.</dc:creator>
	<dc:creator>Deepa, M.</dc:creator>
	<dc:subject xml:lang="en-US">artificial deep neural network</dc:subject>
	<dc:subject xml:lang="en-US">correlation tool</dc:subject>
	<dc:subject xml:lang="en-US">DDoS</dc:subject>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:subject xml:lang="en-US">network intrusion detection system</dc:subject>
	<dc:subject xml:lang="en-US">RF-score</dc:subject>
	<dc:subject xml:lang="en-US">XGBoost-score</dc:subject>
	<dc:description xml:lang="en-US">The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today’s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and  CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2023-12-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/19148</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.17.3.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 17 No. 3 (2023); 292-315</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2023.17.3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/19148/6529</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Journal of ICT Research and Applications</dc:rights>
</oai_dc:dc>