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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) · hlm. 057–064

DOI
10.30598/barekengvol16iss1pp057-064
ISSN
2615-3017, 1978-7227
Terbit
2022-03-21
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng

Abstrak

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%.

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  • Universitas Bhayangkara Surabaya
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Rekan penulisOrganisasiArtikel bersama
Muhammad MashuriAfiliasi tidak diketahui3
Kevin Agung Fernanda RifkiAfiliasi tidak diketahui2
Wibawati WibawatiAfiliasi tidak diketahui2
Tabita Yuni SusantoAfiliasi tidak diketahui2
Tiza Ayu ViraniaAfiliasi tidak diketahui2
Veniola ForestryaniAfiliasi tidak diketahui1
Niam RosyadiAfiliasi tidak diketahui1
Idrus SyahzaqiAfiliasi tidak diketahui1
Hani Brilianti RochmantoAfiliasi tidak diketahui1
Ichwanul Kahfi PrasetyaAfiliasi tidak diketahui1
Hidayatul KhusnaAfiliasi tidak diketahui1
Diaz Fitra AksiomaAfiliasi tidak diketahui1
Novri SuhermiAfiliasi tidak diketahui1
Eirene Christina SellyraAfiliasi tidak diketahui1
Farhan LandiAfiliasi tidak diketahui1
Kevin Agung Fernanda RifkiAfiliasi tidak diketahui1
Muhammad Yahya MatdoanAfiliasi tidak diketahui1
Haryono HaryonoUniversitas Bhayangkara Surabaya1
Rusdarti RusdartiAfiliasi tidak diketahui1
Masrukhi MasrukhiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (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>
</oai_dc:dc>