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INTERPRETABLE PREDICTIVE MODEL OF NETWORK INTRUSION USING SEVERAL MACHINE LEARNING ALGORITHMS

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 1 (2022) · pp. 057–064

DOI
10.30598/barekengvol16iss1pp057-064
ISSN
2615-3017, 1978-7227
Published
2022-03-21
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

Network intrusion is any unauthorized activity on a computer network. Attacks on the network computer system can be devastating and affect networks and company establishments. Therefore, it is necessary to curb these attacks. Network Intrusion Detection System (NIDS) contributes to recognizing the attacks or intrusions. This paper explains the factors that influence network attacks. Some machine learning methods are used such as are logistic regression, random forest XGBoost, and CatBoost. The best model is chosen from these models based on its accuracy level. Classification modeling is divided into two types, namely using a dummy and not using dummy variables. The best method for predicting network intrusion is a random forest with a dummy variable that has an Area Under Curve (AUC) value of 92.31% and an accuracy of 90.38%.

Collaboration network of the authors

17 authors · 30 co-authorship links · 2 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • Universitas Pattimura
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kevin Agung Fernanda RifkiInstitut Teknologi Sepuluh Nopember3
Muhammad MashuriInstitut Teknologi Sepuluh Nopember2
Tabita Yuni SusantoInstitut Teknologi Sepuluh Nopember2
Tiza Ayu ViraniaInstitut Teknologi Sepuluh Nopember2
Veniola ForestryaniInstitut Teknologi Sepuluh Nopember1
Niam RosyadiInstitut Teknologi Sepuluh Nopember1
Idrus SyahzaqiInstitut Teknologi Sepuluh Nopember1
Hani Brilianti RochmantoInstitut Teknologi Sepuluh Nopember1
Ichwanul Kahfi PrasetyaInstitut Teknologi Sepuluh Nopember1
Wibawati WibawatiInstitut Teknologi Sepuluh Nopember1
Eirene Christina SellyraInstitut Teknologi Sepuluh Nopember1
Farhan LandiInstitut Teknologi Sepuluh Nopember1
Muhammad Yahya MatdoanUniversitas Pattimura1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">INTERPRETABLE PREDICTIVE MODEL OF NETWORK INTRUSION USING SEVERAL MACHINE LEARNING ALGORITHMS</dc:title>
	<dc:creator>Ahsan, Muhammad</dc:creator>
	<dc:creator>Anam, Arif Khoirul</dc:creator>
	<dc:creator>Julian, Erdi</dc:creator>
	<dc:creator>Jaya, Andi Indra</dc:creator>
	<dc:description xml:lang="en-US">Network intrusion is any unauthorized activity on a computer network. Attacks on the network computer system can be devastating and affect networks and company establishments. Therefore, it is necessary to curb these attacks. Network Intrusion Detection System (NIDS) contributes to recognizing the attacks or intrusions. This paper explains the factors that influence network attacks. Some machine learning methods are used such as are logistic regression, random forest XGBoost, and CatBoost. The best model is chosen from these models based on its accuracy level. Classification modeling is divided into two types, namely using a dummy and not using dummy variables. The best method for predicting network intrusion is a random forest with a dummy variable that has an Area Under Curve (AUC) value of 92.31% and an accuracy of 90.38%.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-03-21</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/4205</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss1pp057-064</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 057-064</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss1year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/4205/4002</dc:relation>
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