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Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection

Journal of Robotics and Control (JRC) · Vol. 5 No. 5 (2024) · hlm. 1502–1524

DOI
10.18196/jrc.v5i5.22508
ISSN
2715-5072, 2715-5056
Terbit
2024-08-08
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng

Abstrak

This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.

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  • AISSMS Institute of Information Technology
  • Prince Sattam Bin Abdulaziz University
  • University of Al-Ameed
  • Symbiosis International University
  • Sahyadri College of Engineering & Management
  • Al-Farahidi University
  • MIT Academy of Engineering
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Rekan penulisOrganisasiArtikel bersama
Taha A. TahaAfiliasi tidak diketahui4
Abdulghafor Mohammed HashimAfiliasi tidak diketahui4
Abadal-Salam T. HussainAfiliasi tidak diketahui3
Ravi SekharAfiliasi tidak diketahui3
Navaneeth BhaskarSahyadri College of Engineering & Management2
Jamal Fadhil TawfeqAl-Farahidi University2
Shobha S. NikamAISSMS Institute of Information Technology1
Rakhi A. KhedkarAfiliasi tidak diketahui1
Aswathy Maruthompilli AjithkumarMIT Academy of Engineering1
Sachin M. ElgandelwarAISSMS Institute of Information Technology1
Vinayak K. BairagiAISSMS Institute of Information Technology1
Shridevi S. VasekarAfiliasi tidak diketahui1
Aziz NanthaamornphongPrince of Songkla University1
Poh Soon JosephNgINTI International University1
Hissah AlmutairiPrince Sattam Bin Abdulaziz University1
Abdullah AlqahtaniPrince Sattam Bin Abdulaziz University1
Zinah S. JabbarImam Ja’afar Al-Sadiq University1
Baqer A HakimAfiliasi tidak diketahui1
Tareq Hamad AbedAfiliasi tidak diketahui1
Taif. S. HasanAfiliasi tidak diketahui1
Raed Abdulkareem HasanAfiliasi tidak diketahui1
Omer K AhmedAfiliasi tidak diketahui1
Enes BektaşAfiliasi tidak diketahui1
Mohammed M AldabbaghAfiliasi tidak diketahui1
Sarah B. EzzatAfiliasi tidak diketahui1
Faris Hassan TahaUniversity of Mosul1
Hazry DesaAfiliasi tidak diketahui1
Muthanna Ibrahim NeamahAfiliasi tidak diketahui1
Yasin BektaşAfiliasi tidak diketahui1
Khalil Farhan YassinAfiliasi tidak diketahui1
Marwa IbrahimAfiliasi tidak diketahui1
Ziyaad H. SalehAfiliasi tidak diketahui1
Basim Ghalib MejbelAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (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">Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection</dc:title>
	<dc:creator>Mahmood, Rafah Kareem</dc:creator>
	<dc:creator>Mahameed, Ans Ibrahim</dc:creator>
	<dc:creator>Lateef, Noor Q.</dc:creator>
	<dc:creator>Jasim, Hasanain M.</dc:creator>
	<dc:creator>Radhi, Ahmed Dheyaa</dc:creator>
	<dc:creator>Ahmed, Saadaldeen Rashid</dc:creator>
	<dc:creator>Tupe-Waghmare, Priyanka</dc:creator>
	<dc:subject xml:lang="en-US">Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Network Security</dc:subject>
	<dc:subject xml:lang="en-US">Multi-Factor Authentication</dc:subject>
	<dc:subject xml:lang="en-US">Network Intrusion Detection</dc:subject>
	<dc:subject xml:lang="en-US">Machine Learning.</dc:subject>
	<dc:description xml:lang="en-US">This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2024-08-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/22508</dc:identifier>
	<dc:identifier>10.18196/jrc.v5i5.22508</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 5 No. 5 (2024); 1502-1524</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v5i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/22508/9391</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Rafah Kareem Mahmood, Ans Ibrahim Mahameed, Noor Q. Lateef, Hasanain M. Jasim, Ahmed Dheyaa Radhi, Saadaldeen Rashid Ahmed, Priyanka Tupe-Waghmare</dc:rights>
</oai_dc:dc>